Chapter 1: New Age Robotic System

On a Sunday evening, Ananya orders a science book from her home in Jaipur. On Tuesday morning, a delivery van stops at her gate. Two days, and she never saw a single person who handled her parcel.

Think about what happened in between. In many large warehouses today, a low, flat robot slides under a whole shelf, lifts it, and carries it to a person standing at a packing counter. Sorting machines read the address label and push each parcel towards the right city. The van that brought the book may have had a system that warns the driver when the vehicle drifts out of its lane. In the hospital two streets away, a lift is at that moment sensing a person standing in its doorway and holding the doors open.

None of these machines look like the robots in films. Not one of them has a face. Yet each of them senses something, decides what to do next, and acts on its own. That is what this chapter is about.

Diagram

Fig. 1.0-1 – photo collage of four everyday robotic systems: a flat shelf-carrying warehouse robot, a robot vacuum cleaner on a floor, a quadcopter drone in flight, and a lift door with its safety sensor edge visible

Curiosity Corner

Q1. What makes a new age robot different from an ordinary machine that has been in a factory for years? Q2. How does a warehouse robot find the right shelf when nobody is guiding it? Q3. If a car can brake by itself, is the driver still needed? Q4. Why have new age robotic systems become useful and possible only in recent years? Q5. A lift has no arms and no wheels on the road, so why can it still be called a robotic system?

Keep these five questions in mind as you read. The chapter answers every one of them.

1.1 From Ordinary Machines to New Age Robotic Systems

Think of a mixer grinder in your kitchen. You press a button, the blades spin, and they keep spinning at the same speed until you switch it off. It does not matter whether the jar is full of tomatoes or almost empty. The mixer has no way of knowing.

Almost every machine works like this. It converts electricity into some useful action, and it repeats that action exactly the same way every time. This is very useful, but it has a clear limit: the machine cannot notice anything about the world around it.

Machines that notice

Now think of a machine that does notice. A robot vacuum cleaner moving across a floor has to know when a chair leg is in front of it, and turn away. To do that, it needs three things working together.

First, it needs a sensor — a part that measures something in the surroundings, such as distance, light, heat or sound, and converts it into an electrical signal. Second, it needs a small computer inside that reads those signals and works out what to do next. Third, it needs an actuator — a part that produces movement, such as a motor that turns a wheel. You will study all three of these in detail in later chapters.

When these three are joined into one working unit, we call it a robotic system. The vacuum senses, then computes, then moves, then senses again. This cycle repeats many times every second, which is why the machine appears to respond to the room.

Diagram

Fig. 1.1-1 – a closed loop of three labelled blocks arranged in a circle with arrows: “Sense (sensors read the surroundings)” → “Decide (controller computes the next action)” → “Act (actuators move the machine)”, with the arrow from Act curving back to Sense; label the loop “repeats continuously”

Definition

A robotic system is a machine that senses its surroundings, computes what to do from those readings, and acts through motors or other moving parts — repeating this cycle on its own.

How It Works

the sense–decide–act loop What it is: The basic working cycle of every robotic system, from a floor cleaner to a factory arm. How it works: Sensors turn something physical, such as distance, into numbers. A program in the controller uses those numbers to select an action. Actuators carry out that action, which changes the surroundings, so the sensors read new values. Used for: A robot vacuum reading distance to a chair leg, then steering its wheels away from it. Watch out: The loop is only as good as its sensors. If a sensor is dusty, blocked or badly placed, the machine will act confidently on a wrong reading.

The older generation of robots

Robots have worked in factories for many years. These older industrial robots were arms bolted to the floor that repeated one exact path — weld here, lift there, place it down — thousands of times a day. They were fast, strong and extremely reliable, but they were also blind in a useful sense. They followed a stored set of instructions and nothing else.

Because of this, they had to be kept inside a safety cage. If a person or an unexpected box came into the path, the arm would swing through it anyway. The surroundings had to be arranged perfectly for the robot, rather than the robot adapting to the surroundings.

Side Note

The first industrial robot arm, called Unimate, began work on a General Motors car assembly line in 1961. It lifted hot metal parts — a job that was dangerous for a person, and always the same movement.

What “new age” means

A new age robot is one that can work in surroundings that are not arranged for it. It carries many sensors, so it builds a picture of what is actually in front of it right now. Its programs can use past data to improve how it responds. Many of them are built to work near people rather than behind a cage.

The change, then, is not that these machines are faster or stronger. It is that they can handle a situation their designers did not describe in advance. A warehouse robot meets a fallen carton, a delivery drone meets a strong wind, a lift meets a rush of people after school. The machine still does not think in the way you do. It computes an answer from readings and stored data — but it computes that answer while the situation is happening.

Curiosity Corner

Q1. What makes a new age robot different from an ordinary machine that has been in a factory for years?

A. An older factory machine repeats one fixed set of movements and cannot notice anything around it. A new age robot senses its surroundings continuously, computes its next action from those readings, and can therefore cope with situations that were not planned for in advance — including working safely near people.

1.2 Key Features of New Age Robotic Systems

Section 1.1 said that a new age robot can cope with a situation nobody planned for. That is easy to say and hard to build. What actually goes into such a machine?

Nine features appear again and again in these systems. No single robot has all nine. A floor cleaner in a house and a robot arm in a hospital are built very differently, but each one is put together from this same set of ideas.

Autonomy and AI

The first feature is autonomy — the ability of a machine to select its next action from its own sensor readings instead of waiting for a human instruction at every step.

To do that, the machine needs software that can handle messy, incomplete information. This is the job of artificial intelligence (AI): computer methods that let a machine carry out tasks that normally need human judgement, such as recognising an object in a photograph or choosing a route through a crowded room.

One branch of AI matters most here. In machine learning (ML), a program is given a large number of past examples and works out the pattern in them by itself, instead of a programmer writing out every rule. A parcel-sorting system that has seen thousands of label photographs can read a new, slightly crooked label. Nobody wrote a rule for “crooked”.

Definition

Autonomy is a machine’s ability to work out and carry out its next action from its own sensor readings, without a person directing each step.

Working alongside people

Older factory robots were kept behind a cage. Many new machines are built to share the same table or floor as a person. A robot designed for this is called a cobot, short for collaborative robot. It moves more slowly, senses touch and pressure, and stops when something unexpected presses against it. Cobots have a chapter of their own later in this book.

Along with this comes human-robot interaction — the ways a person and a machine exchange information. A cleaning robot may take a spoken command, a hospital delivery robot may show a screen and wait for a tap, and a service robot may respond to a wave of the hand. The exchange works in both directions: the machine also has to tell the person what it is about to do.

Making sense of many sensors

One sensor is rarely enough. A camera fails in the dark. An ultrasonic sensor cannot tell a person from a pillar. So new age robots carry several sensors at once and combine their readings into a single picture of the surroundings. This is called sensor fusion (see Fig. 1.2-1).

Diagram

Fig. 1.2-1 – three labelled input boxes on the left, “Camera (shape and colour)”, “Distance sensor (how far)” and “Wheel sensor (how far travelled)”, with arrows feeding into one central box labelled “Sensor fusion in the controller”, and a single arrow out of it to a box labelled “One combined picture of the surroundings”

How It Works

sensor fusion What it is: A method of combining readings from two or more different sensors into one description of the surroundings. How it works: Each sensor measures a different quantity at the same moment. The controller lines these readings up in time and merges them, giving more weight to the sensor that is more reliable in the present conditions. Used for: A delivery robot on a footpath uses a camera to identify a pole and a distance sensor to fix how far away it is. Watch out: Fusion cannot rescue a sensor that is silently wrong. If a dirty camera keeps reporting a clear path, the merged picture is confidently wrong.

Moving and handling

Two more features describe the body of the robot rather than its software.

Mobility and navigation covers how a robot moves and how it works out where it is. Wheeled bases are common indoors, legged designs handle steps and rubble, tracked bases grip loose ground, and aerial designs fly over everything. Navigation is the harder half: the machine must keep track of its own position on a map while it moves.

Manipulation and grasping covers how a robot holds things. Older grippers were two stiff metal fingers, suited to identical steel parts. Newer grippers use softer materials and pressure sensors, so the same hand can pick up a tomato and a screwdriver without crushing either.

Many robots on one task

A single large robot is not always the best answer. Swarm behaviour means using many small, simple robots that share information and work as a group. Each one follows simple local rules — keep clear of your neighbours, cover ground that nobody has covered — and useful group behaviour comes out of that.

Swarms are used for searching a wide area after a disaster, for surveying farmland, and for coordinated drone displays. The advantage is that losing one unit does not stop the task. A group of thirty can finish the work with twenty-nine.

Closely linked to this is distributed intelligence, where the computing is spread across many parts instead of sitting in one central processor. A robot arm may have a small processor in each joint handling that joint’s own balance, while the main computer only sets the overall goal. Spreading the work this way keeps the machine responding quickly, and a fault in one part does not freeze the whole system.

Ideas borrowed from nature

The last feature is bio-inspired design — copying the way an animal or insect moves or behaves. Insects walk over uneven ground without falling; fish move through water using very little energy. Robots built on these ideas are used for underwater inspection and for search and rescue in collapsed buildings, where wheels would be useless.

Copying nature has limits worth remembering. A robot fin can imitate the shape and motion of a fish, but not a fish’s muscles or its ability to heal, so bio-inspired machines usually match one specific ability rather than the whole animal.

1.3 Warehouse Robots

A large warehouse may hold lakhs of different items on shelves that run for hundreds of metres. When an order comes in for three items, somebody has to find all three, bring them together, pack them and send them out — within hours.

Done entirely on foot, most of that time is walking. The worker is not slow; the building is simply large. This is the problem warehouse robots were built to solve, and it explains their strange shape. They are not arms or humanoids. Most of them are low, flat platforms whose only job is to move shelves and boxes around a building.

Definition

A warehouse robot is a robotic system used inside a warehouse or distribution centre to move, sort, store or count goods, working from instructions sent by a central computer.

Finding the way across the floor

A warehouse robot does not wander and search. The building is prepared for it. Printed markers — often square codes stuck to the floor in a grid — are read by a camera pointing downwards as the robot passes over them. Each marker tells the robot exactly where it is.

Between two markers, the robot counts wheel turns to estimate how far it has moved, and corrects that estimate at the next marker. Meanwhile a central computer knows which shelf holds the ordered item, and sends the robot a target position and a route. Distance sensors on the robot handle whatever the map does not know about, such as another robot crossing its path or a carton that has fallen off a shelf (see Fig. 1.3-1).

Diagram

Fig. 1.3-1 – top view of a warehouse floor drawn as a grid of square floor markers; a low flat robot shown under a shelf, with a dashed route line from the shelf position to a “picking station” where a person stands; label the robot’s downward-facing camera, and show a second robot crossing with an arrow labelled “distance sensor detects and robot waits”

Curiosity Corner

Q2. How does a warehouse robot find the right shelf when nobody is guiding it?

A. The floor is marked with codes that the robot reads with a downward-facing camera, so it always knows its own position. A central computer stores which shelf holds which item and sends the robot the target position and a route. The robot follows that route, counting wheel turns between markers, and uses its distance sensors to avoid anything that is not on the map.

1.3.1 Common Types of Warehouse Robots

Different jobs inside the building need different machines. These are the types you will most often meet.

Automated Guided Vehicles (AGVs) are the oldest and simplest. They follow a fixed path laid out for them — a magnetic strip, a painted line or a wire under the floor — and carry a load from one point to another along it. If a box blocks the line, an AGV stops and waits.

Autonomous Mobile Robots (AMRs) carry a map of the building instead of following a line. When something blocks the way, an AMR computes a fresh route around it and keeps going. This makes it more useful in a busy warehouse, but also more expensive and harder to set up.

Compare Table

Table 1.1 – AGV vs AMR

Feature	Automated Guided Vehicle (AGV)	Autonomous Mobile Robot (AMR)
How it knows the route	Follows a fixed physical guide such as a magnetic strip or painted line	Uses a stored map and its own sensors to work out a route
Changing the route	The strip or line has to be relaid	Changed in software
Meeting an obstacle	Stops and waits for it to be cleared	Plans a path around it
Cost and setup	Lower cost, simpler to install	Higher cost, more setting up
Suits	Repeating the same trip along a fixed lane	A changing floor plan with people and other robots moving about

Goods-to-person robots slide under a whole shelving unit, lift it, and bring it to a person waiting at a picking station. The person stays in one place and the shelves come to them.

Sorting robots send each parcel to the right destination. A small tilting or belt-topped robot carries one parcel across a platform and drops it into the chute for that city or that delivery route.

Packaging robots place items into boxes, seal them and stick on the label. Heavy-duty robots handle pallets and large containers, including loading and unloading trucks — loads no person should lift.

Inventory management robots count what is actually on the shelves. They drive the aisles reading barcodes with a camera, or reading RFID (radio frequency identification) tags, which are small tags that reply with an identity number when a radio signal reaches them. Because the tag does not have to be seen, a whole shelf can be counted in seconds.

Cleaning and maintenance robots keep the floors clear. A slippery or dusty floor is a hazard to workers and a source of wheel slip for every other robot in the building.

Example 1.1

how much faster is goods-to-person picking

Given: Walking to the shelf and back, a worker takes 90 s to fetch one item. With shelves brought to the picking station, the same worker takes 30 s per item.

Find: How many items each method handles in one hour, and the difference.

Working:

One hour = 60 × 60 = 3600 s

On foot: 3600 s ÷ 90 s per item = 40 items

Goods-to-person: 3600 s ÷ 30 s per item = 120 items

Difference: 120 − 40 = 80 items

40 items per hour on foot against 120 items per hour with goods-to-person picking — 80 more items per hour, or three times as many.

1.3.2 Benefits of Warehouse Robots

Increased efficiency. Robots can run through the night and through holidays, pausing only to charge. Orders that arrive at 2 a.m. are picked at 2 a.m.

Accuracy. A machine reading a barcode does not confuse two similar packets at the end of a long shift. Fewer wrong items are picked, so fewer parcels come back.

Savings on manual work. Fetching and lifting is the largest part of the labour in a warehouse, and it is the part robots take over. The staff who remain move to packing, checking and handling the awkward orders.

Intelligent path planning. Path planning is software working out the best route from where the robot is to where it must go. With every robot’s position known to one computer, routes can be chosen so that machines do not queue at the same aisle.

Scalability. When orders rise before a festival, more robots are switched on. When the rush ends, they are switched off. There is no hiring and training cycle for a two-week peak.

Safety. Repeated lifting and bending injures backs over years. Handing those tasks to machines removes a real cause of long-term harm to workers.

Real-time data. Every robot reports what it did and where it is. The warehouse computer therefore knows the true stock count at any moment, not just what was counted last month.

Set against all this, the honest limits are the cost of installing the system, the need for a fairly flat and well-marked floor, and the fact that a fault in the central computer can stop every robot at once. A worker with a trolley never stops for a software update.

Ethics Watch

The same system that tracks robots can track how fast each worker picks, item by item. Used well, that data finds bottlenecks in the building. Used badly, it turns into constant pressure on individuals. Warehouses that handle it responsibly tell staff clearly what is being recorded, and judge teams over a shift rather than counting seconds against one person.

1.4 Assistant Robots

Warehouse robots work in a building arranged for them. Now think about a machine that has to work in an ordinary home, shop or hospital corridor — places arranged for people, full of pets, school bags and moved furniture.

Machines built for these places are called assistant robots. Their purpose is not speed or strength. It is to take over jobs that are repetitive, tiring, or awkward for a person to keep doing every single day.

Definition

An assistant robot is a robotic system that helps people with everyday tasks in the places where they live and work, rather than in a space built specially for robots.

Around the house

Domestic robots handle household chores. The common ones are floor vacuuming and mopping machines, and lawn mowing machines for a garden. They map the space over several runs, remember where the walls and steps are, and return to a charging point when the battery falls low.

Their limit is the untidy floor. A wire, a sock or a spilled dal will stop a cleaning robot or spread the mess, because the machine can detect that something is there without being able to identify what it is.

Talking to a machine

Many assistant robots take spoken instructions. “Set a reminder for six o’clock” is a very different input from a button press, and it takes two steps of software to handle (see Fig. 1.4-1).

First, speech recognition converts the sound of the voice into written words. Then natural language processing (NLP) — the set of AI methods that let a computer work with ordinary human sentences — pulls the instruction out of those words: the action is “set reminder”, the time is 6:00. Only then can the machine act.

Diagram

Fig. 1.4-1 – four labelled blocks left to right with arrows between them: “Microphone (sound)” → “Speech recognition (sound to text)” → “Natural language processing (text to instruction: action + time)” → “Action carried out (reminder stored)”

Helping older people at home

Elderly care robots are built around a small number of genuinely useful jobs: reminding a person when a medicine is due, keeping track of readings such as pulse, raising an alarm after a fall, and giving physical support while walking.

For a person living alone, an alert that reaches a family member within minutes matters more than anything clever the machine does.

Ethics Watch

An elderly care robot records health readings and, in many designs, watches the room continuously. That data says a great deal about one person’s private life. Anyone setting up such a system should be clear with the user about what is recorded, who can see it, and how long it is kept — and a machine should add to human company, never quietly replace the visits that keep someone connected to other people.

Out in public

Personal assistant robots manage schedules, set reminders, answer questions and send messages. Retail robots in a store guide a customer to the right aisle, read out product information, and support self-checkout counters.

Service robots work in hospitality. In hotels they carry items to rooms; in museums they guide visitors from one gallery to the next; at airports they answer questions about gates and timings in several languages.

Agriculture robots work in fields — sowing seeds at even spacing, harvesting fruit, and carrying cameras along rows so that a farmer can spot a diseased patch of crop while it is still small.

Companion robots are designed for company rather than chores. They respond to speech and touch and are used mainly with people who are isolated or unwell. It is worth being precise about what such a machine does: it detects speech and movement and responds with stored behaviour. It does not feel anything about the person.

Assistant robots also work in classrooms and in hospitals. Both of these settings are large enough to deserve sections of their own, and you will meet them in 1.6 and 1.9.

1.5 Smart Homes

A ceiling fan has one useful setting and one problem. You set the speed when you go to bed, the room cools down by two in the morning, and the fan carries on at the same speed because nobody told it otherwise.

A smart home closes that gap. Sensors around the house measure things like temperature, light, motion and door position. A small controller — often a hub box, a phone app or a voice speaker — reads those measurements and switches devices on and off according to rules the family has set.

Definition

A smart home is a house in which sensors, appliances and a controller are connected together, so that devices can be switched or adjusted from stored rules and from readings of the surroundings, rather than only by hand.

How a rule actually runs

The connection between the parts is usually wi-fi or a similar short-range radio link, which lets each device send its readings to the hub and receive instructions back. Once that link exists, a rule is simply a sentence of the form “when this reading crosses this value, do that” (see Fig. 1.5-1).

Diagram

Fig. 1.5-1 – a central box labelled “Home hub (controller)” with two-way arrows to four labelled boxes around it: “Temperature sensor”, “Motion sensor”, “Door sensor” on the input side, and “Fan, lights, air conditioner” on the output side; a caption box beside the hub reading “Rule: if temperature above 30 °C, switch fan on”

How It Works

an automatic fan rule in a smart home What it is: A stored rule that switches a fan using a temperature reading instead of a person’s hand. How it works: The sensor sends the room temperature to the hub every few seconds. The hub compares each reading with the value set in the rule. When the reading crosses that value, the hub sends a switch-on instruction to the fan’s smart plug, and a switch-off instruction when the temperature falls back below it. Used for: Keeping a room comfortable overnight without anyone waking up to change the speed. Watch out: A sensor placed in direct sunlight or right above a stove reads far higher than the rest of the room, so the fan runs when it is not needed.

Safety Note

Smart plugs and smart switches connect to the mains supply. Fitting or opening one is work for a qualified electrician, not a school project. Never open a wall switch or a plug point, and never work on any mains-powered device.

What a smart home is used for

Home automation is the largest use: lighting that dims in the evening, an air conditioner that starts cooling shortly before the family reaches home, and appliances that run during off-peak hours. Cleaning machines, which you met in 1.4, are usually joined to the same network, so a floor cleaner can be started from a phone.

Security and surveillance is the second use. Door and window sensors report when something opens. Motion sensors and cameras report movement, send an alert to a phone, and let the owner view the camera from anywhere.

Personal assistance is the third. Because these homes usually include a voice speaker, the same spoken instruction can control many devices at once — the speech recognition and language processing of 1.4, now wired to the lights and the fan. Families also use this to keep an eye on children and older members of the house, and on pets left at home during the day.

Ethics Watch

Home cameras and voice speakers record inside private spaces, and they record guests and domestic helpers who never agreed to it. Anyone setting one up should keep cameras out of bedrooms and bathrooms, tell visitors that recording is happening, and check what the maker stores on its own servers rather than on the device.

Try Yourself

List six electrical devices in your home. For each one, write whether it can sense anything about the room, and what single reading would make it more useful — light level, temperature, movement or time of day.

1.6 Smart Schools

A teacher explaining fractions to forty students is doing one thing at once, for forty people who are at different points. Some finished the idea five minutes ago; two are still stuck on the first step. There is no way to give each of them a different next question.

That is the gap a smart school tries to narrow. Robotics and AI are used here not to replace the teacher, but to handle the parts that do not scale well — repeated practice at the right level, routine record-keeping, and marking that takes hours.

Learning tools you can touch

Robot kits are used to teach the subject you are studying now. Small programmable robots and kits such as Lego Mindstorms let a student write a short program and watch a real motor turn, which makes an idea like “sense, then decide” concrete in a way a page cannot.

Humanoid robots such as NAO and Pepper are used in classrooms as speaking, moving demonstration machines. A robot that responds to a gesture holds attention, and students then work out how it did so.

Interactive boards do the same at the front of the room. A board that recognises writing and gestures lets a group work on the same surface, and lets the day’s work be saved rather than wiped off.

Practice at the right level

Personalised learning means a system that adjusts what it gives a student next, based on what that student has already got right and wrong (see Fig. 1.6-1).

The method is simple to describe. Each answer is recorded. The software looks at the recent pattern — which type of question is being missed — and picks the next question from the group that matches. A student who keeps missing subtraction with borrowing gets more of those, while a student who cleared them moves on.

Diagram

Fig. 1.6-1 – a loop of four labelled blocks: “Student answers a question” → “Answer stored (correct / wrong, and question type)” → “Software finds the weak topic in recent answers” → “Next question chosen from that topic”, with an arrow from the last block back to the first

Records, marking and safety

Automated attendance uses a camera at the door with face recognition software, so the register fills itself as students walk in.

Automated administrative tools mark objective tests, total the scores and return the result at once. Feedback that arrives on the same day is far more useful than feedback that arrives next week.

Game-based learning platforms turn practice into levels and points to keep younger students working through drill material that is otherwise dull.

Safety monitoring uses cameras in corridors and laboratories to spot smoke, a fall, or a crowd forming, and alert staff quickly.

Virtual and augmented reality puts a student inside a three-dimensional model — walking through a historical site, or watching blood move through a heart at a size no textbook diagram can show.

Ethics Watch

Face recognition for attendance stores the face measurements of children, which cannot be changed if the data leaks the way a password can. Camera monitoring in corridors records every student all day. A school using these should collect the smallest amount of data that does the job, keep cameras out of changing rooms and washrooms, delete recordings on a fixed schedule, and tell students and parents plainly what is stored and who can see it.

Two honest limits are worth stating. These systems need reliable electricity, internet and maintenance, which not every school has. And face recognition is less accurate on some faces than others, depending on the photographs the software was built from — so an attendance system should always allow a student marked absent by the camera to be marked present by a teacher.

1.7 Smart Mobility

Getting from one place to another is a problem every city solves badly. Buses arrive in pairs after a long gap. A signal stays green over an empty road while a queue builds on the other side. A driver circles a market for ten minutes looking for a parking space.

Smart mobility is the use of sensors, data and connected systems to move people and goods more efficiently and more safely. Very little of it looks like a robot, but the sense–decide–act loop of 1.1 is behind all of it.

Some familiar examples: traffic signals that count waiting vehicles and change their timing to suit; buses that report their position continuously so an app can show when yours will reach the stop; metro trains that run under automatic control with staff supervising; and route apps that read traffic reports from thousands of phones and send you around a jam.

The vehicle itself is the part that has changed most, and that is the rest of this section.

1.7.1 Autonomous Cars and Driver-Assisted Cars

These two terms are often mixed up, and the difference matters.

A driver-assisted car keeps the person in charge. The car watches the road alongside the driver and steps in to warn or to correct — but the driver is driving, and is responsible for the vehicle at every moment.

An autonomous car is built to carry out the whole driving task itself, within the conditions it was designed for: reading the road, steering, accelerating, braking and following traffic rules without a person doing those things.

Compare Table

Table 1.2 – Driver-assisted car vs autonomous car

Feature	Driver-assisted car	Autonomous car
Who performs the driving	The driver	The vehicle’s own system
Who must watch the road	The driver, at all times	The system, within its designed conditions
What the system does	Warns, brakes or steers briefly to help	Plans and carries out the whole journey
Hands and attention	Both stay on the task	Not required while the system is in control
Where you find it today	Widely sold on ordinary cars	Limited to tested areas and trial services

What the car senses with

Whichever type it is, the car needs a picture of everything around it, day and night, and sensor fusion (1.2) puts that picture together from four kinds of sensor.

Cameras see lane markings, traffic signs and the colour of a signal. Radar measures the distance and speed of vehicles ahead and works through rain and fog. LIDAR (light detection and ranging) sends out laser pulses and times their return, building an accurate three-dimensional map of the surroundings. GPS (Global Positioning System) uses signals from satellites to fix the car’s position on a map. Ultrasonic sensors cover the last metre or so, which is what parking needs.

Diagram

Fig. 1.7-1 – top view of a car with shaded sensing zones drawn around it: a long narrow zone ahead labelled “radar – distance and speed of vehicle ahead”, a wide zone ahead labelled “camera – lanes, signs, signals”, a rotating 360° zone labelled “LIDAR – 3D map of surroundings”, two side zones behind the mirrors labelled “blind spot sensors”, and short zones at the bumpers labelled “ultrasonic – parking”

Curiosity Corner

Q3. If a car can brake by itself, is the driver still needed?

A. In a driver-assisted car, yes. Automatic braking is a safety net for the moments when a person cannot react in time; the driver is still driving and is still responsible. Only a fully autonomous vehicle carries out the driving task itself, and even then only in the conditions it was designed and tested for.

Common Error

treating a driver-assist feature as self-driving What goes wrong: A car holds its lane and keeps its distance on a highway, so the driver stops watching the road. The system then meets something it was not built for — a faded lane marking, a stopped truck, heavy rain — and there is nobody paying attention. The fix: Read what the feature is called and what it claims. Lane keeping and cruise control assist a driver; they do not replace one. Attention stays on the road.

1.7.2 Features of Advanced Driver Assistance Systems

The collection of these safety features on a car is called Advanced Driver Assistance Systems (ADAS). Each one solves a specific, well-defined problem.

Adaptive Cruise Control (ACC) holds a set speed and also holds a safe gap to the vehicle in front, slowing down and speeding up as that vehicle does.

Lane Departure Warning (LDW) watches the lane markings through the camera and warns — usually a sound or a vibration in the steering wheel — when the car drifts across a line without the indicator being used.

Automatic Emergency Braking (AEB) applies the brakes when a collision is about to happen and the driver has not reacted. It cannot always prevent the crash, but reducing the speed of impact reduces the injury.

Blind Spot Monitoring (BSM) watches the areas beside and behind the car that the mirrors do not cover, and shows a light in the mirror when a vehicle is sitting there.

Parking assistance uses the ultrasonic sensors and rear camera to measure a parking space and guide the car into it, with some systems steering while the driver controls the speed.

Traffic sign recognition reads speed limit and no-entry signs from the camera image and displays them on the dashboard, so a limit is not missed.

Driver monitoring points a small camera at the driver and tracks eye and head position for the signs of drowsiness, prompting a break.

Emergency assistance goes one step further: if the driver stops responding altogether, the system slows the car, brings it to a stop and switches on the hazard lights.

How It Works

Adaptive Cruise Control What it is: A cruise control that keeps a set gap to the vehicle ahead instead of only a set speed. How it works: A forward-facing radar measures the distance to the vehicle ahead and how fast that distance is changing. The controller compares this with the gap the driver has selected and adjusts the throttle, or applies the brakes, to hold that gap. With the road clear, it returns to the set speed. Used for: Long highway drives, where holding a steady gap by foot is tiring. Watch out: Radar sees the vehicle directly ahead. A motorcycle cutting in from the side, or a vehicle stopped dead in the lane, is a much harder case, so the driver must keep watching.

Example 1.2

how far a car travels while the driver reacts

Given: Speed = 60 km/h. Typical driver reaction time before the foot even reaches the brake = 1.0 s. An automatic braking system reacts in about 0.1 s.

Find: The distance covered during each reaction, and the difference.

Working:

Convert the speed: 60 km/h = 60 × 1000 m ÷ 3600 s = 16.67 m/s

Driver: 16.67 m/s × 1.0 s = 16.67 m

System: 16.67 m/s × 0.1 s = 1.67 m

Difference: 16.67 − 1.67 = 15.0 m

The car covers about 16.7 m before a driver starts braking and about 1.7 m before the system does — roughly 15 m of extra distance, which is why automatic braking reduces the force of an impact.

1.8 Autonomous Drones

To check whether a crop is diseased, a farmer walks the rows. To check a mobile tower for cracks, a worker climbs it. To find out which villages are cut off after a flood, someone has to reach them.

All three jobs are slow, and the second is dangerous. All three become much easier from thirty metres up, which is what a drone gives you at low cost.

A drone is an aircraft with no pilot on board. When it also carries out its flight without someone steering it moment by moment, it is called an autonomous drone.

Definition

An autonomous drone is an unmanned aircraft that flies a planned route by itself, using satellite positioning and its own sensors to navigate and to avoid obstacles.

Flying a planned route

The flight is planned before take-off. On a map, the operator marks a series of positions the drone must pass through, called waypoints, along with the height and the speed for each leg (see Fig. 1.8-1).

In the air, GPS tells the drone where it is. The controller compares that position with the next waypoint and adjusts the speed of the four rotors to move towards it. When it arrives, it switches to the following waypoint. Take-off and landing run the same way, from the recorded home position.

Diagram

Fig. 1.8-1 – map view of a rectangular field with a dashed flight path in a lawnmower pattern passing through numbered waypoints 1 to 6; a “home point” marked at one corner with take-off and return arrows; a small callout showing the drone with the label “GPS fixes position, controller steers to next waypoint”

How It Works

waypoint navigation What it is: The method by which a drone flies a route on its own. How it works: The route is stored as a list of positions. In flight, GPS gives the current position several times a second. The controller works out the direction and distance to the next waypoint and adjusts rotor speeds to close that gap, moving down the list until the route ends. Used for: Photographing a field in even strips, so the images can be joined into one map. Watch out: GPS accuracy drops between tall buildings and under dense tree cover, where satellite signals are blocked or reflected. The drone then believes it is somewhere it is not.

What else these drones do

Obstacle avoidance is handled by sensors facing forward, downward and sideways. A route drawn on a map does not know about a new electric pole or a tree branch, so the drone must detect and steer around whatever the map missed.

Automated take-off and landing removes the hardest part of manual flying. The drone lifts off, flies the route and lands back on the home point, and returns there by itself if the battery runs low or the radio link drops.

Remote control remains available. An autonomous drone is not cut off from its operator. The person can take manual control at any moment, which is required for safety.

Real-time data transmission sends video and sensor readings to the ground while the drone is still flying — the difference between a rescue team seeing a flooded road now and seeing it after the drone lands.

Payload integration means the drone carries useful equipment: an ordinary camera, a thermal camera, LIDAR for mapping ground shape, or a small package for delivery.

AI-based recognition works on those images. Software trained on past pictures marks the patches of crop that look diseased, or counts vehicles in a photograph, so a person examines a short list instead of nine hundred images.

Example 1.3

how long to survey a field

Given: A field measures 300 m by 200 m. The drone flies at 5 m/s and its camera covers a strip 20 m wide.

Find: The flying time needed to cover the whole field once.

Working:

Area of field = 300 m × 200 m = 60 000 m²

Area covered each second = 5 m/s × 20 m = 100 m² per second

Time = 60 000 m² ÷ 100 m² per second = 600 s

600 s ÷ 60 = 10 min

About 10 minutes of flying, not counting turns at the ends of each strip.

Safety Note

Drone rotors turn fast enough to cut skin, so never reach towards a drone that is powered on, and always disconnect the battery before changing a propeller or any part. Flying is also regulated: drones must be registered, and flight is restricted near airports, defence areas and crowds. Any school flying should be outdoors, in a permitted open area, with a teacher present.

Ethics Watch

A camera drone can look into windows, courtyards and terraces that are private from the ground, and the people below cannot tell what is being recorded. Fly over your own or permitted ground, avoid pointing the camera into homes, and delete footage of people captured by accident.

1.9 Robotics for Medicine and Healthcare

Every human hand shakes a little. You cannot see it while writing, but at the tip of an instrument working inside a body, a tremor of a fraction of a millimetre matters. A surgeon also tires during a three-hour operation, and a specialist sitting in one city cannot examine a patient in another.

Robots are used in hospitals to reduce exactly these limits — steadiness, reach, and the endless repetition of laboratory and pharmacy work. In almost every case the machine is a tool in the hands of a trained person, not a replacement for one.

Robots in the operating theatre

A surgical robot is a set of thin robotic arms holding instruments and a camera, controlled by a surgeon sitting at a console beside the operating table. Systems of this kind, such as the da Vinci surgical system, are in use in hospitals in India and elsewhere.

The surgeon moves the console controls, and the arms copy those movements — but scaled down, so a two-centimetre hand movement becomes a two-millimetre instrument movement, and with the natural tremor of the hand filtered out. The camera gives a magnified view from inside the body (see Fig. 1.9-1).

Diagram

Fig. 1.9-1 – left side shows a surgeon seated at a console with hand controls and a viewing screen; an arrow labelled “movements scaled down, tremor filtered” leads to the right side, showing a trolley with three thin robotic arms over a patient, two holding instruments and one holding a camera; a return arrow labelled “magnified camera view” goes back to the console screen

Because the instruments are thin, they enter through small cuts instead of one long opening. This is minimally invasive surgery, and its advantages follow directly from the smaller cut: less blood loss, less pain afterwards, less scarring and a shorter stay in hospital.

The limits are real. These systems are expensive, they need a trained team and regular servicing, and the surgeon feels far less of the resistance of tissue than with instruments held directly in the hand. Because of the cost, they are found in large hospitals rather than in small clinics.

Reaching patients at a distance

Telemedicine is medical consultation carried out over a video link. A telemedicine robot is a screen and camera on a wheeled base that a doctor drives through a ward from another location, stopping at each bed to see the patient and speak to the staff there.

For a district hospital with no resident cardiologist, this is the difference between a specialist opinion today and one next week. It also has a plain limit: a doctor at the far end of a video link cannot feel a pulse or press on an abdomen, so a person must still be present to examine the patient.

Recovery, monitoring and the laboratory

Rehabilitation robots support a limb while a patient recovers movement after an injury or a stroke. Recovery needs the same movement repeated hundreds of times, which is tiring for both patient and therapist. The machine guides the limb along the correct path, adds only as much help as is needed, and records how many repetitions were completed and how steady they were.

Patient monitoring systems watch measurements continuously — pulse, breathing rate, blood oxygen, temperature — and raise an alert when a reading crosses a set limit. A nurse cannot stand beside every bed at three in the morning; a monitor can watch every bed at once and call for the nurse who is needed.

Laboratory automation handles blood and tissue samples: moving tubes, measuring out exact quantities, running tests and recording results. The gain is not only speed. A machine labels and tracks every sample by barcode, so two samples are far less likely to be swapped.

Pharmacy dispensing robots pick medicines from store, check them against the prescription by barcode and pass them to the pharmacist. Wrong medicines and wrong strengths are among the most common serious mistakes in a hospital, and an automatic check on every packet reduces them.

Ethics Watch

Hospital systems hold some of the most sensitive information about a person — diagnoses, test results, continuous monitoring records. This data should be seen only by the staff treating that patient, kept encrypted, and never used to judge someone outside their treatment. Responsibility also stays human: when a monitor raises an alert or a system suggests a reading, a qualified clinician makes the decision and answers for it.

1.10 Why New Age Robotic Systems Are Relevant and Possible

You have now seen these machines in warehouses, homes, schools, vehicles, fields and hospitals. Taken together they are called New Age Robotic Systems (NARS) — robotic systems that sense their surroundings, use AI methods to work out what to do, and operate in ordinary human spaces.

Two separate questions are worth asking about them. Why do we want them now? And why could they not be built thirty years ago? The answers are different.

Why they are relevant

Work that harms people over time. Lifting cartons all day, climbing towers, spraying chemicals in a field, working near hot metal — these jobs injure people slowly. Handing them to machines removes a cause of harm rather than a person.

Work nobody can keep doing at a steady standard. A person checking labels stays accurate for the first hour and drifts by the eighth. A machine reading the same label is equally accurate at 4 a.m.

Demand that has grown faster than the workforce. Online orders now arrive in numbers that no amount of hiring can pick by hand within a day. The same pressure exists in healthcare, where the number of older patients needing regular monitoring is rising in most countries.

Reaching places people cannot reach quickly. After a flood or an earthquake, a drone maps blocked roads in minutes. A specialist doctor joins a ward hundreds of kilometres away over a video link.

Better decisions from better data. A machine that senses continuously also records continuously. A farmer sees which corner of the field is drying out; a warehouse knows its true stock now, not last month.

Curiosity Corner

Q4. Why have new age robotic systems become useful and possible only in recent years?

A. They are useful because work that is repetitive, dangerous or larger than the available workforce has grown — in warehouses, farms, transport and healthcare. They are possible because four things arrived at once: cheap, small sensors made for mobile phones; small processors powerful enough to run AI methods on board; batteries that store far more energy for their weight; and machine learning methods together with the large stores of data they need. None of these was available in a low-cost form thirty years ago.

Why they are possible now

Cheap sensors. Cameras, distance sensors and motion sensors are made in enormous numbers for mobile phones. A robot builder buys the same parts at the same low price, which was not the case when each sensor was a specialist item.

Small, powerful computers. A board the size of a playing card can now run image recognition on live video while drawing a few watts. Earlier, that work needed a machine too large and too power-hungry to carry.

Better batteries. Rechargeable lithium-ion cells store far more energy for their weight than older types. A drone that flies for twenty minutes or a floor cleaner that works for an hour is a battery achievement as much as a robotics one.

Wireless networks. Wi-fi and mobile data let a robot receive instructions, send video and share a map with other machines without a cable.

Machine learning and data. The methods of ML need large numbers of examples. Digital cameras and connected devices produced those examples, and only then could a program be built that recognises a crooked label or a diseased leaf.

Cheap prototyping. Low-cost controller boards and 3D printing let a small team build and test a design in days. Building anything used to mean a machine shop.

Diagram

Fig. 1.10-1 – five labelled boxes across the bottom, “Cheap sensors”, “Small powerful processors”, “Better batteries”, “Wireless networks” and “Machine learning and data”, each with an arrow rising into one box at the top labelled “New Age Robotic Systems (NARS)”

Compare Table

Table 1.3 – A factory robot of the 1980s vs a new age robotic system

Feature	Factory robot of the 1980s	New age robotic system
Sensing	Limited — position of its own joints	Many sensors, readings combined by sensor fusion
Computing	A fixed stored sequence of movements	On-board programs that compute the next action from live readings
Where it works	Inside a safety cage, on a fixed floor plan	In ordinary homes, roads, wards and fields
Power	Mains cable	Rechargeable battery, in most mobile designs
Improving it	Reprogramming by an engineer	Software updates, and models retrained on new data

Side Note

The small sensor that lets a phone know it has been turned sideways is the same kind of part that tells a drone which way is level. Robotics did not create that sensor; it inherited it from the phone industry, at the price the phone industry pays.

The honest side of the picture

None of this means every task should be given to a machine. Robots take over some jobs entirely, and the people who did those jobs need other work and training to move into it — a real cost that falls on particular families, not on an average.

These systems also fail in ways that ordinary machines do not. They depend on power, on network links and on software that can be wrong in ways nobody noticed. Any responsible design keeps a manual alternative and a person who can take over.

1.11 The Elevator as a Robotic System

An elevator, or lift, has no arms, no wheels on a road and no camera looking at you. It is one of the most ordinary machines in any tall building. Yet everything this chapter has described is present in it.

Look at what happens when you press a call button. Something records your request, works out when to serve it against everyone else’s requests, moves a heavy car to your floor, opens a door, senses whether anyone is standing in the doorway, and closes it again. That is the sense–decide–act loop of 1.1, running all day.

The parts, in robotics language

Input from people. The call buttons outside and the floor buttons inside are how a passenger states what is wanted. Displays and voice announcements are how the lift replies. This is human-machine interaction, in a simpler form than a service robot but the same idea.

Sensors. A position sensor tells the controller which floor the car is at and where it is between floors. A door sensor — usually an infrared beam across the opening — detects a person or a bag in the doorway. A load sensor measures the weight in the car and triggers the overload alarm. A speed sensor watches how fast the car is moving.

Controller. A small computer holds the list of pending requests and decides which floor to serve next, in what order, and when to open and close the doors.

Actuators. The hoist motor raises and lowers the car. A separate motor opens and closes the doors.

Diagram

Fig. 1.11-1 – block diagram with boxes left to right: “Input: call buttons and floor buttons” → “Controller (decides next floor, door timing)” → “Actuators: hoist motor and door motor” → “Lift car moves”; a feedback arrow returns from a box labelled “Sensors: floor position, door beam, load, speed” back into the controller

Deciding which floor to serve next

The interesting part is the decision. Three people press buttons on three floors within a few seconds. Serving them in the order the buttons were pressed would send the car up, down and up again, wasting time for everyone.

Most lifts therefore follow a simple rule: keep going in the present direction, serving every request that lies ahead in that direction, and only then turn around.

Algorithm

choosing the next floor for a lift Input: The current floor, the current direction of travel, and the list of floors requested so far. Output: The next floor the car will stop at. Steps:

1. If there are no requests, stay where you are.
2. Look at the requests that lie ahead in the current direction.
3. If any exist, go to the nearest one of them and stop there.
4. If none exist, reverse the direction.
5. Look at the requests in the new direction and go to the nearest one.
6. Remove each request from the list once it has been served, and repeat.

Why it works: Serving every request along the way before turning means the car passes each floor once per sweep, so no passenger is left waiting while the car crosses their floor without stopping.

Trace it once. The car is at floor 2 and moving up, and requests come in for floors 5, 3 and 1. Ahead in the up direction are 3 and 5, so the car stops at 3, then at 5. Only then does it reverse and come down to 1. The button for floor 1 was pressed second, but it is served last — and every passenger’s total wait is shorter than if the car had zig-zagged.

Safety, and learning from use

Lifts carry a set of safety systems that work whether or not the main controller is functioning: mechanical brakes that grip the guide rails if the car moves too fast, buffers at the bottom of the shaft, an alarm button and an intercom, and a fire mode that brings the car to the ground floor and holds the doors open.

Some modern installations go further. In a busy building, the controller records the pattern of requests over weeks and positions idle cars where calls are most likely at that hour — near the ground floor at nine in the morning, near the canteen floor at one. This is the same idea as machine learning: past data changing future behaviour, without anyone writing a new rule.

Curiosity Corner

Q5. A lift has no arms and no wheels on the road, so why can it still be called a robotic system?

A. Because it has all the parts of one. It takes input from people, senses its own position, the weight it carries and anything blocking its doors, computes which floor to serve next from those readings, and acts through its hoist and door motors — repeating the cycle continuously. Looking like a robot was never part of the definition.

Safety Note

Never try to force lift doors open or hold them with your hands or a bag, and never step into a car whose floor is not level with yours. If a lift stops between floors, stay inside, use the alarm and wait for help. During a fire, use the stairs — a lift may stop at the floor where the fire is.

Try Yourself

Stand near the automatic door or the lift in your school or a nearby building for five minutes. Write down one thing it senses, one decision it makes from that reading, and one action it takes. Then note one situation it clearly cannot handle.

1.12 Summary

An ordinary machine repeats a fixed action and cannot notice anything around it. A robotic system senses its surroundings, computes what to do from those readings, and acts through motors — repeating this sense–decide–act loop continuously.
The three working parts of a robotic system are sensors, a controller that runs the program, and actuators.
Older industrial robots followed a stored sequence inside a safety cage. New age robots work in spaces that are not arranged for them and can handle situations not planned for in advance.
Nine features describe new age robotic systems: autonomy with AI, cobots and human-robot interaction, sensor fusion, mobility and navigation, manipulation and grasping, swarm behaviour, distributed intelligence and bio-inspired design.
Autonomy means selecting the next action from live sensor readings, not working without a program.
Machine learning lets a program find patterns in past examples instead of a programmer writing every rule.
Sensor fusion combines readings from several different sensors into one picture of the surroundings, because no single sensor is reliable in all conditions.
Warehouse robots move, sort, store and count goods, guided by floor markers and a central computer that assigns tasks and routes.
Automated Guided Vehicles follow a fixed physical guide; Autonomous Mobile Robots carry a map and plan their own way around obstacles.
Other warehouse types include goods-to-person, sorting, packaging, heavy-duty, inventory management and cleaning robots.
Warehouse robots bring efficiency, accuracy, savings on manual work, intelligent path planning, scalability, safety and real-time stock data — against high installation cost and a central point of failure.
Assistant robots help people in ordinary homes, shops and public places. They include domestic, elderly care, personal assistant, retail, service, agriculture and companion robots.
Spoken instructions are handled in two steps: speech recognition turns sound into text, and natural language processing turns that text into an instruction.
A smart home links sensors, appliances and a controller so that devices act on stored rules and live readings. Its main uses are home automation, security and surveillance, and personal assistance.
Smart schools use robot kits, interactive boards, personalised learning, automatic attendance, automatic marking, safety monitoring and virtual reality. Face data and camera records need careful handling.
Smart mobility uses sensing and data to move people and goods better — adaptive signals, tracked buses, automatic metro control and route apps.
A driver-assisted car keeps the person responsible for driving; an autonomous car carries out the driving task itself within the conditions it was designed for.
Vehicle sensing combines cameras, radar, LIDAR, GPS and ultrasonic sensors.
ADAS features include Adaptive Cruise Control, Lane Departure Warning, Automatic Emergency Braking, Blind Spot Monitoring, parking assistance, traffic sign recognition, driver monitoring and emergency assistance.
Autonomous drones fly stored waypoint routes using GPS, avoid obstacles with their own sensors, take off and land automatically, send data in real time, carry payloads, and use AI to recognise objects in their images.
Medical robots give steadiness, reach and consistency: surgical systems controlled by a surgeon, minimally invasive procedures, telemedicine, rehabilitation, patient monitoring, laboratory automation and pharmacy dispensing.
New Age Robotic Systems (NARS) became possible because cheap sensors, small powerful processors, better batteries, wireless networks and machine learning with large stores of data arrived together.
They became relevant because of harmful and repetitive work, demand growing faster than the workforce, places people cannot reach quickly, and the value of continuous data.
An elevator is a robotic system: it senses position, load, doorway and requests, decides the order of floors, and acts through its hoist and door motors.
A lift chooses the next floor by serving all requests ahead in its present direction before reversing, which shortens the total waiting time for everyone.
Every one of these systems has honest limits — cost, dependence on power and networks, sensors that can be wrong, and privacy or fairness risks that must be handled deliberately.

1.13 Key Terms

Actuator — a part that produces movement in a machine, such as a motor that turns a wheel.
Adaptive Cruise Control (ACC) — a driver assistance feature that holds a set speed and also holds a safe gap to the vehicle ahead.
Advanced Driver Assistance Systems (ADAS) — the set of safety features on a car that warn or assist the driver, such as automatic braking and lane warning.
Agriculture robot — a robot used in fields for tasks such as sowing, harvesting and monitoring crop health.
Artificial intelligence (AI) — computer methods that let a machine carry out tasks normally needing human judgement, such as recognising an object or choosing a route.
Automated administrative tools — school software that marks objective tests, totals scores and returns results automatically.
Automated attendance — recording who is present using a camera and face recognition software instead of a manual register.
Automated Guided Vehicle (AGV) — a warehouse robot that carries loads along a fixed physical guide such as a magnetic strip or painted line.
Automatic Emergency Braking (AEB) — a feature that applies the brakes when a collision is about to happen and the driver has not reacted.
Autonomous car — a vehicle built to carry out the whole driving task itself within the conditions it was designed for.
Autonomous drone — an unmanned aircraft that flies a planned route by itself using satellite positioning and its own sensors.
Autonomous Mobile Robot (AMR) — a warehouse robot that carries a map of the building and plans its own route around obstacles.
Autonomy — a machine’s ability to work out and carry out its next action from its own sensor readings, without a person directing each step.
Bio-inspired design — building a robot by copying the way an animal or insect moves or behaves.
Blind Spot Monitoring (BSM) — a feature that watches the areas beside and behind a car that the mirrors do not cover and warns the driver.
Cleaning and maintenance robot — a robot that keeps warehouse floors clear and safe for workers and other machines.
Cobot — short for collaborative robot; a robot built to work safely in the same space as a person.
Companion robot — a robot designed to respond to speech and touch and provide company, mainly for people who are isolated or unwell.
Distributed intelligence — spreading the computing of a robotic system across many parts instead of one central processor.
Domestic robot — a robot that handles household chores such as vacuuming, mopping or mowing a lawn.
Driver-assisted car — a car in which the person drives and is responsible, while the system warns or briefly corrects.
Driver monitoring — a feature that tracks a driver’s eye and head position for signs of drowsiness or distraction.
Elderly care robot — an assistant robot that reminds an older person about medicines, tracks health readings, raises alarms and supports movement.
Emergency assistance — a feature that slows and stops a car and switches on hazard lights if the driver stops responding.
Game-based learning platform — teaching software that turns practice material into levels and points to keep students working through it.
Global Positioning System (GPS) — a system that fixes a receiver’s position on the earth using signals from satellites.
Goods-to-person robot — a warehouse robot that lifts a whole shelving unit and brings it to a person at a picking station.
Heavy-duty robot — a warehouse robot built to handle pallets, large containers and truck loading.
Human-robot interaction — the ways in which a person and a machine exchange information, such as speech, gestures, screens and buttons.
Inventory management robot — a robot that counts stock on shelves by reading barcodes or RFID tags.
Laboratory automation — machines that move, measure, test and track medical samples in a laboratory.
Lane Departure Warning (LDW) — a feature that warns the driver when the car drifts across a lane marking without the indicator being used.
LIDAR (light detection and ranging) — a sensor that sends out laser pulses and times their return to build a three-dimensional map of the surroundings.
Machine learning (ML) — a branch of AI in which a program finds patterns in a large number of past examples instead of following rules written by a programmer.
Manipulation and grasping — how a robot holds and handles objects, including soft grippers with pressure sensors.
Minimally invasive surgery — surgery carried out through small cuts using thin instruments and a camera, giving less pain and faster recovery.
Mobility and navigation — how a robot moves and how it works out its own position on a map.
Natural language processing (NLP) — AI methods that let a computer work with ordinary human sentences and pull instructions out of them.
New age robot — a robot that can work in surroundings not arranged for it, sensing continuously and coping with unplanned situations.
New Age Robotic Systems (NARS) — the whole family of modern robotic systems that sense their surroundings, use AI methods to compute what to do, and work in ordinary human spaces.
Packaging robot — a warehouse robot that places items in boxes, seals them and applies labels.
Parking assistance — a feature that measures a parking space with ultrasonic sensors and a camera and guides the car into it.
Path planning — software working out the best route from a robot’s present position to where it must go.
Patient monitoring system — a hospital system that measures vital signs continuously and raises an alert when a reading crosses a set limit.
Personal assistant robot — a robot that manages schedules, sets reminders, answers questions and sends messages.
Personalised learning — teaching software that chooses each student’s next question from the pattern of that student’s earlier answers.
Pharmacy dispensing robot — a machine that picks medicines from store and checks them against the prescription by barcode.
Radio frequency identification (RFID) — a small tag that replies with an identity number when a radio signal reaches it, so items can be counted without being seen.
Rehabilitation robot — a machine that supports and guides a limb through repeated exercises during recovery, recording the repetitions.
Retail robot — a robot in a store that guides customers, gives product information and supports checkout.
Safety monitoring — the use of cameras and software in a school to detect smoke, falls or crowding and alert staff.
Sensor — a part that measures something in the surroundings, such as distance, light, heat or sound, and converts it into an electrical signal.
Sensor fusion — combining readings from two or more different sensors into a single description of the surroundings.
Service robot — a robot used in hospitality settings such as hotels, museums and airports to carry items and guide visitors.
Smart home — a house in which sensors, appliances and a controller are connected, so devices act on stored rules and live readings.
Smart mobility — the use of sensors, data and connected systems to move people and goods more efficiently and safely.
Sorting robot — a warehouse robot that carries a parcel across a platform and drops it into the chute for its destination.
Speech recognition — software that converts the sound of a voice into written words.
Surgical robot — thin robotic arms holding instruments and a camera, controlled by a surgeon at a console.
Swarm behaviour — many small robots following simple local rules and sharing information so that the group completes a task together.
Telemedicine — medical consultation carried out over a video link, often through a wheeled screen-and-camera robot in a ward.
Traffic sign recognition — a feature that reads road signs from the camera image and displays them on the dashboard.
Virtual and augmented reality — three-dimensional environments in which a student can explore a place or a process that cannot be brought into the classroom.

1.14 Objective Questions

Fill in the blanks.
1. A part that measures something in the surroundings and converts it into an electrical signal is called a ____________.
2. Combining readings from two or more different sensors into one picture of the surroundings is called ____________.
3. The short form NARS stands for ____________.
State whether each statement is true or false.
1. An autonomous robot works without any program inside it.
2. An Automated Guided Vehicle plans a fresh route around a box that blocks its path.
3. In a driver-assisted car, the driver remains responsible for the vehicle.
4. A surgical robot decides by itself where to make each cut.
Which of these is not one of the three working parts of a robotic system?
1. Sensor
2. Controller
3. Actuator
4. Printer
A drone loses its radio link to the operator during a survey flight. The stored fallback behaviour is to:
1. continue the route and land wherever the battery runs out
2. return to the home point recorded before take-off
3. switch off the rotors immediately
4. hover in the same place until the battery ends
Which sensor on a car keeps working well in heavy rain and fog to measure the distance and speed of the vehicle ahead?
1. Camera
2. Radar
3. Ultrasonic sensor
4. GPS receiver
Match each robot in Column A with the job it does in Column B.

Column A	Column B
a. Automated Guided Vehicle	i. Counts stock on shelves by reading tags
b. Goods-to-person robot	ii. Carries a load along a fixed magnetic strip
c. Sorting robot	iii. Lifts a whole shelving unit and brings it to a worker
d. Inventory management robot	iv. Drops each parcel into the chute for its destination

A lift is at floor 2 and moving up. Requests are waiting at floors 5, 3 and 1. Using the rule a lift controller follows, write the order in which it will serve the three floors.

1.15 Concept Questions

Define autonomy. Explain why an autonomous machine still needs a program.
Give one reason why a robot carries several different sensors instead of one very good sensor. Support your answer with an example of two sensors that fail in different conditions.
Differentiate between an Automated Guided Vehicle and an Autonomous Mobile Robot, using any three points of difference.
A drone flies at 4 m/s and its camera covers a strip 25 m wide. Work out the flying time needed to cover a field measuring 400 m by 250 m once. Show your working and give the answer in minutes.
Explain the two software steps that turn a spoken instruction into an action in an assistant robot. What goes wrong when the room is noisy, and why?
“An elevator is a robotic system.” Justify this statement by naming what it senses, what it computes and what it acts through. Then state one way in which it is simpler than a warehouse robot.

1.16 Case-Based Questions

A distribution centre outside Jaipur packs online orders. At present, a worker walks to the shelves and takes 120 s to fetch one item. The centre is considering goods-to-person robots, after which the same worker would stay at a picking station and take 40 s per item. A shift lasts 4 hours of picking time.
1. Calculate how many items one worker handles in a shift by each method, and the difference between the two.
2. Explain how a goods-to-person robot finds the correct shelf when nobody is guiding it.
3. State one benefit of the new system for the workers themselves, and one risk that the centre should handle responsibly.
A school is setting up two new systems. The first is a camera at the gate that records attendance using face recognition. The second is practice software that chooses each student’s next question from that student’s earlier answers.
1. Describe how the practice software decides which question to give next.
2. Suggest two precautions the school should take with the face data it collects, and give a reason for each.
3. Explain why a teacher must still be able to mark a student present by hand, even after the camera system is working.