Automated & Emerging Tech 🤖
Self-parking cars, robot farmers, machines that diagnose illness and filters that learn what spam looks like. Program a 3D robot arm, drive with adaptive cruise control, and train your own AI.
🎯 What you'll unlock this chapter
- ⚙️ How sensors, microprocessors and actuators make automated systems work — in industry, transport, farming, weather, gaming, lighting and science
- 🦾 What makes something a robot, end-effectors, and robots in every area of life
- 🧠 Artificial intelligence, expert systems and machine learning
- 🎮 A 3D programmable robot arm, an adaptive cruise control simulator, an expert system and a spam filter that learns
- ✏️ All 13 coursebook activities and every worked example
⚙️ Sensors, microprocessors and actuators 6.1.1
An automated system is a combination of hardware and software designed to work automatically, with no human intervention (though humans often still monitor it).
- Sensors (input devices) take readings from the surroundings — analogue data goes through an ADC.
- The microprocessor processes the data and decides what to do, based on its program and stored values.
- It sends signals to actuators (via a DAC if needed) to control motors, wheels, solenoids, pumps and valves.
🏭 Industry 6.1.2
☢️ Nuclear power station
A distributed control system (DCS) — a powerful computer — monitors and controls the whole process. Temperature, pressure, flow, gas-escape and radiation sensors send data (through ADCs) to the DCS, which checks it against its database and sends signals to actuators to operate pumps, valves — or an emergency shutdown.
💊 Making paracetamol
Process 1 manufactures the paracetamol; process 2 presses the tablets. Sensors on both send data to a central computer, which checks it is within the correct parameters and acts through actuators — giving more efficient use of materials and a more consistent product.
✅ Advantages
- • Much faster to act than a human
- • Safer — timely interventions and keeps people away from danger
- • Runs at optimum conditions — small changes spotted quickly
- • Cheaper in the long run (fewer staff monitoring 24/7)
- • More consistent product, less waste
⚠️ Disadvantages
- • Expensive to set up and test
- • Conditions never tested for could still occur — so a supervisor is needed
- • Open to cyberattacks
- • Needs enhanced (expensive) maintenance
🚗 Transport 6.1.2
Trains use automatic signal control (sensors by the track; if a driver passes a red signal, actuators apply the brakes); aircraft use autopilots.
🅿️ Self-parking cars
Sensors and cameras measure the parking spaces; the computer tells the driver when one is big enough. On "auto-park", actuators operate the steering, brakes and throttle. ✅ Fits smaller spaces, fewer scratches, stops for any new object (e.g. a child), consistent. ⚠️ Drivers lose skills; dirty/faulty sensors give false data; can kerb the wheels; expensive; extra maintenance.
🛣️ Adaptive cruise control
The driver sets a cruising speed. Lasers in the bumper bounce off the vehicle in front; the computer calculates the distance (and the car in front's speed). Too close → actuators reduce the throttle or brake. Far enough away → it speeds back up to the set speed.
🎮 Adaptive cruise control
Your car (blue) cruises at the set speed. The car in front (grey) speeds up and slows down. The on-board computer compares the laser distance with the stopping distance for your speed, and works the throttle and brakes.
🌾 Agriculture — automatic irrigation 6.1.2
In Brazil, fields over 10 km² are irrigated automatically. A controller receives data from an automatic weather station every ten minutes, and ultrasonic water-level sensors in the channels send readings by wireless. The controller opens and closes valves and gates through actuators — and different crops can be given different conditions (rice likes flooding, orange trees like it dry).
✅ Fewer workers (one supervisor for a huge area), better control, saves water, faster response, safer (fields can reach 40 °C). ⚠️ Expensive to set up, high maintenance with specialist technicians, and blocked or collapsed channels aren't detected.
🌦️ Automated weather stations 6.1.2
They save labour and gather constant data from remote places. A microprocessor, storage, a (usually solar-charged) battery and sensors: thermometer (temperature), anemometer (wind speed), hygrometer (humidity), barometer (air pressure), level sensor (rainfall) and light sensor (hours of daylight). Data and calculated values go to a central database; near airports, reports go to pilots every five minutes. The only actuator is the tipping-bucket rain gauge: at set times a piston tips the collected rain into a vessel where a level sensor measures it.
🎮 Gaming, 💡 lighting and 🧪 science 6.1.2
Gaming
Accelerometers respond to tilting the controller; proximity sensors in touch pads detect hand and finger position. With a microcontroller they make games more immersive by simulating real movement.
Lighting
Garden lights switch on when it gets dark (light sensor) or when someone enters (infrared/motion sensor, also used for security); a fountain light show runs pumps through actuators. ✅ Automatic control, lower energy use, safer wireless (no trailing wires), longer bulb life, new displays programmable. ⚠️ Expensive to set up; wireless less reliable; more maintenance.
Science laboratory
Two chemicals react; liquid is added from a burette whose tap is opened by an actuator. Level sensors measure how much is added and a colorimeter checks the colour; when it turns bright orange, the microprocessor closes the tap. ✅ Repeatable, less dangerous, faster (many experiments at once), automatic analysis, fewer trained staff, monitor from anywhere. ⚠️ Less flexible, security risks if data is shared, expensive equipment.
Adding AI lets systems store vast amounts of facts, learn from data that would overwhelm humans and spot patterns we'd miss — but it changes the skills needed and depends on its training data.
🦾 What is robotics? 6.2.1
"Robot" comes from the Czech robota (forced labour). Robotics is the branch of computer science that brings together the design, construction and operation of robots. You'll find them in factories (welding, spray-painting, fitting windscreens, precise cutting, bottling, warehouses), in the home (floor sweepers, lawn mowers, pool and window cleaners, "friend" robots) and in the air as drones (UAVs — remote-controlled or autonomous, for aerial photography and parcel delivery).
✅ Characteristics of a robot 6.2.2
| Characteristic | How |
|---|---|
| 1 · Senses its surroundings | Sensors (light, pressure, temperature, acoustic…) tell it the size, shape and weight of objects, whether something is hot or cold…; the data goes to a microprocessor |
| 2 · Has a degree of movement | Wheels, cogs, pistons and gears let it turn, twist, grip and lift; motors, hydraulics and actuators; end-effectors — attachments for specific jobs like welding, spraying, cutting or lifting |
| 3 · Is programmable | A controller ("brain") decides what to do using data from sensors and cameras, and can be programmed for different tasks |
Robotics and AI are mostly separate fields — many robots just repeat tasks with no AI. And software "robots" (web crawlers, chatbots) aren't true robots: they don't move or sense the physical world.
🧊 3D · Program a robot arm
Move the joints with the sliders and press Save position to store each one as an instruction (or load the demo). Then Run program. Swap the end-effector, and untick the car to see the sensor stop the robot.
🏭 Robots in industry 6.2.3
Paint spraying and welding car bodies, making microchips and electrical goods, automatic warehouses. They're controlled by embedded microprocessors or a linked computer, and programmed either with a sequence of instructions, or by a worker who guides the arm (or wears sensors on their own arm) while each movement is stored as an instruction. Sensors stop them doing "stupid things" — like spraying when no car is there, or carrying on when the paint runs out.
✅ Advantages
- • Work in dangerous conditions; work 24/7 without breaks
- • More productive and more accurate
- • Consistent results every time
- • Do boring, repetitive tasks; lower running costs (fewer staff)
⚠️ Disadvantages
- • Can't do "unusual" tasks or think for themselves
- • Expensive to buy and set up; need maintenance
- • Job losses; risk of cyberattacks
- • Production can be moved to wherever is cheapest
🚘 Transport, 🌱 agriculture, 🏥 medicine, 🏠 home, 🎢 entertainment 6.2.3
Autonomous vehicles
Cameras capture the surroundings; radar, ultrasonics and LiDAR (light detection and ranging, using lasers) build a 3D picture even in rain, fog or darkness. The microprocessor checks its database (e.g. a red light means brake) and sends signals to actuators for steering, brakes, throttle and gears. ✅ Safer (no human error), better for the environment, less congestion, more lane capacity, shorter journeys, stress-free parking. ⚠️ Expensive; hacking; software glitches; sensors fail in snow; dirty cameras; public reluctance; job losses (taxi drivers). Driverless trains: punctual, energy-efficient, more frequent — but high cost, passenger behaviour and CCTV needed. Pilotless planes: fewer pilot errors, better aerodynamics — but hacking, glitches and security worries.
Agriculture
Harvesting (only ripe fruit; cameras guide the cutter), weed control (AI tells weeds from crops; GPS keeps it on the rows), phenotyping (3D models of plants to check health, using machine learning), seed-planting and fertiliser drones (even cloud seeding), and autonomous mowers and sprayers.
Medicine
Surgery (safer, quicker), monitoring patients, disinfecting wards, taking blood (less painful, safer for staff), microbots delivering drugs to a precise site, and robotic prosthetic limbs.
Home & entertainment
Autonomous vacuum cleaners (proximity sensors and cameras), lawn mowers, window cleaners, personal assistants; theme-park rides, camera robots for films and sport, robot toys and companions.
🧠 Artificial intelligence 6.3.1–6.3.2
AI is the branch of computer science that simulates intelligent human behaviour — the cognitive functions of the brain (acquiring knowledge and understanding through thought, experience and the senses), measured against human benchmarks like reasoning, speech and sight. At heart, AI is a collection of rules and data, plus the ability to reason, learn and adapt.
| Narrow AI | General AI | Strong AI |
|---|---|---|
| Better than a human at one specific task | About as good as a human at a specific task | Better than a human at many tasks |
Examples: news generated from live feeds; smart assistants (Alexa, Siri…) that recognise speech and get better as they learn; chatbots; autonomous cars; facial expression recognition (mapping landmarks like eyebrow and mouth corners to emotions).
🩺 Expert systems 6.3.3
An expert system mimics the decision-making of a human expert, using knowledge and inference. It asks a series of questions — each depending on earlier answers — then gives a conclusion, suggested actions and the probability that it's right, and can explain its reasoning. Uses: oil and mineral prospecting, medical diagnosis, fault diagnosis, tax and finance, chess, delivery routing, identifying plants, animals and chemicals.
| Part | Role |
|---|---|
| User interface | How the user interacts — usually yes/no questions |
| Inference engine | The main problem-solving part: like a search engine, it asks questions and searches the knowledge base for a match, using the rules base |
| Knowledge base | A repository of facts — a collection of objects and their attributes |
| Rules base | The inference rules, e.g. IF continent = "South America" AND language = "Portuguese" THEN country = "Brazil" |
| Explanation system | Tells the user the reasoning behind the conclusion |
The user interface and inference engine together form the expert system shell. Setting one up: gather knowledge from experts and books → build the knowledge base → write the rules base → set up the inference engine → build the user interface → test with known outcomes.
✅ Advantages
- • High level of expertise and accuracy; consistent
- • Stores vast numbers of facts; can have several areas of expertise
- • Traceable logic; very fast; unbiased; gives a probability
⚠️ Disadvantages
- • Users need lots of training
- • Very expensive to set up and maintain
- • "Cold" responses; only as good as the facts entered
- • Users may wrongly assume it's infallible
🎮 Expert system: what animal is it?
Think of an animal from the knowledge base below and answer the questions. The inference engine picks each question based on your earlier answers, then the explanation system shows its reasoning.
Show the knowledge base (objects and attributes)
📈 Machine learning 6.3.3
Machine learning is a subset of AI: algorithms are trained on data and learn from past experience and examples, so they can make predictions or decisions about new, unseen data without being specifically programmed for it. It can analyse huge volumes of complex data that would take humans years.
| AI | Machine learning |
|---|---|
| Simulated intelligence in machines | Getting machines to make decisions without being programmed to do so |
| Aim: machines that think like humans | Aim: machines that learn from data, so they can solve new problems |
Examples: a search engine treating a click on page 1 as a success and learning from it; spam filtering (clean the data by removing "stop words", learn which words appear in spam, train on known spam, test, go live); collaborative filtering ("customers who bought… also bought…"); and fraud detection (gather data → remove redundant data → train on real examples → build the model → test with known outcomes).
🎮 Train a spam filter
The filter has learned word scores from its training emails. Type a new email to see its prediction. If it's wrong, teach it — and watch it learn.
✏️ Practice hub 13 activities · every example
Every activity from the coursebook chapter — auto-marked where there's a right answer, with model answers for the written parts. Fresh questions every time you hit 🔀 New questions.
📘 Worked examples
Every type of worked example in the chapter, step by step. Hit 🔀 Another example for a new one.
🃏 Key-term flashcards Tap to flip
🧠 Mini-quiz
📝 Exam-style questions Try, then peek
- ✅ A temperature sensor sends readings (via an ADC) to the microprocessor continually.
- ✅ The microprocessor compares each reading with the stored pre-set value.
- ✅ Too cold → signals (via a DAC) to actuators switch the heater on / close the windows; too hot → heater off / motors open the windows.
- ✅ This repeats continuously (feedback), with no human intervention.
- ✅ Can sense its surroundings using sensors.
- ✅ Has a degree of movement (wheels, gears, actuators, end-effectors).
- ✅ Is programmable — a controller decides its actions.
- ✅ Independent: no direct human control (autonomous) and can replace the human activity completely.
- ✅ Dependent: a human interfaces with it directly, and it supplements rather than replaces human activity.
- ✅ User interface — the user answers (yes/no) questions and sees the results.
- ✅ Inference engine — the problem-solving part; asks questions based on earlier answers and searches the knowledge base for a match.
- ✅ Knowledge base — a repository of facts: objects and their attributes.
- ✅ Rules base — the inference rules (IF… AND… THEN…) used in matching.
- ✅ Explanation system — explains the reasoning behind the conclusion.
- ✅ Data about emails is collected — content, headers, sender.
- ✅ The data is cleaned (stop words and punctuation removed).
- ✅ The model is trained with a data set of emails known to be spam / not spam, learning which words and features indicate spam.
- ✅ It’s tested, fine-tuned and used on new emails — and keeps learning as users mark emails.
- ✅ Advantages: safer (removes human error); less congestion and better traffic flow; better for the environment; stress-free parking.
- ⚠️ Disadvantages: expensive; risk of hacking; software glitches; sensors can fail in snow or if dirty; public reluctance; job losses.
🚀 Going further A Level preview
Deep learning is a subset of machine learning. It arranges algorithms in layers — an input layer, one or more hidden layers and an output layer — to form an artificial neural network of units (nodes) loosely based on neurons in the brain. It needs huge amounts of training data and computing power, and is great at complex tasks like recognising birds or faces from the pixel values in an image — but its reasoning can be hard to explain (a "black box").