AI Foundations: What robots are and how robotic systems work

What This Is

A robot is a programmable machine that can sense its environment, process information, and perform physical actions—autonomously or semi-autonomously—to complete tasks. In professional work, robots automate repetitive, dangerous, or precision-critical tasks, freeing humans for higher-value work. Example: A collaborative robot (cobot) in a car factory tightens bolts on an assembly line while workers handle complex quality checks.

Key Facts & Principles

• Sense-Plan-Act Cycle: Robots operate in a loop: sense (gather data via sensors), plan (process data to decide actions), and act (execute movements or tasks). Example: A warehouse robot uses LiDAR to detect obstacles, plans a path around them, and moves to pick an item.
• Degrees of Freedom (DoF): The number of independent movements a robot can make. More DoF = more flexibility but also complexity. Example: A robotic arm with 6 DoF can reach any point in 3D space and rotate its gripper.
• Actuators: Components that convert energy (electric, hydraulic, pneumatic) into motion. Example: A servo motor in a robot arm controls precise joint movements.
• Sensors: Devices that collect data about the robot’s environment (e.g., cameras, force sensors, temperature probes). Example: A self-driving forklift uses ultrasonic sensors to avoid collisions.
• Control System: The "brain" of the robot, often a microcontroller or computer running algorithms to process sensor data and send commands to actuators. Example: A PLC (Programmable Logic Controller) in a packaging robot coordinates conveyor belts and robotic arms.
• End Effector: The tool or device at the end of a robot arm (e.g., grippers, welders, 3D printers). Example: A vacuum gripper picks up flat objects like glass sheets in manufacturing.
• Autonomy Levels: Robots range from teleoperated (human-controlled) to fully autonomous (self-deciding). Example: A surgical robot is teleoperated by a surgeon, while a Roomba vacuum cleans autonomously.
• Kinematics: The study of motion without considering forces. Forward kinematics calculates the end effector’s position from joint angles; inverse kinematics does the reverse. Example: A robot arm’s software uses inverse kinematics to determine how to move joints to reach a specific point.
• Feedback Loop: Sensors provide real-time data to adjust actions (e.g., correcting grip force if an object slips). Example: A robotic hand uses force sensors to avoid crushing a fragile item.
• Human-Robot Interaction (HRI): Design principles for safe and intuitive collaboration between humans and robots. Example: Cobots have rounded edges and speed limits to prevent injuries.

Step-by-Step Application

1. Define the Task
2. Break down the job into discrete steps. Example: For a robot packing boxes, steps might include: (1) identify box size, (2) pick item, (3) place item in box, (4) seal box.

3. Ask: Is this task repetitive, dangerous, or precision-dependent? If yes, it’s a good candidate for automation.

4. Select the Right Robot Type

5. Match the robot to the task:

   • Fixed-arm robots: High precision (e.g., welding, assembly).
   • Mobile robots: Transport goods (e.g., autonomous forklifts).
   • Cobots: Safe for human collaboration (e.g., packaging, inspection).
   • Soft robots: Handle delicate items (e.g., food processing).

6. Choose Sensors and Actuators

7. Pick sensors based on needs:
   • Vision: Cameras for object recognition.
   • Force/torque: For delicate handling.
   • Proximity: To avoid collisions.

8. Select actuators for required speed/precision (e.g., servo motors for accuracy, pneumatic cylinders for speed).

9. Program the Control System

10. Use teach pendant (manual programming) for simple tasks or offline programming (simulation-based) for complex ones.
11. For autonomy, integrate machine learning (e.g., computer vision to identify defects) or path-planning algorithms (e.g., A* for navigation).

12. Example: Program a cobot to pick parts from a conveyor using a camera and gripper.

13. Test and Iterate

14. Run the robot in a simulated environment first (e.g., ROS Gazebo, NVIDIA Isaac Sim).
15. Test in real-world conditions with safety protocols (e.g., emergency stops, speed limits).

16. Refine based on sensor feedback (e.g., adjust grip force if items are dropped).

17. Deploy and Monitor

18. Integrate with existing systems (e.g., ERP for inventory tracking).
19. Set up dashboards to monitor performance (e.g., cycle time, error rates).
20. Schedule preventive maintenance (e.g., recalibrate sensors monthly).

Common Mistakes

• Mistake: Assuming all robots are fully autonomous. Correction: Most industrial robots are programmed for specific tasks and lack adaptability. Use AI/ML only if the task requires dynamic decision-making (e.g., sorting mixed items).

• Mistake: Ignoring safety standards (e.g., ISO 10218 for industrial robots). Correction: Always follow safety protocols (e.g., light curtains, speed limits, emergency stops). Example: A cobot should stop if a human enters its workspace.

• Mistake: Overlooking sensor calibration. Correction: Sensors drift over time. Recalibrate regularly (e.g., LiDAR in autonomous vehicles) to avoid errors like misaligned pick-and-place actions.

• Mistake: Underestimating integration costs. Correction: Budget for software (e.g., ROS), training, and downtime during deployment. Example: A $50K robot may require $20K in programming and testing.

• Mistake: Neglecting human factors. Correction: Design intuitive interfaces (e.g., touchscreens, voice commands) and train staff to work alongside robots. Example: A warehouse worker should know how to override a robot in an emergency.

Practical Tips

• Start small: Pilot a single-task robot (e.g., a cobot for screwdriving) before scaling to complex systems.
• Leverage off-the-shelf solutions: Use pre-built robotic cells (e.g., for palletizing) to reduce customization costs.
• Prioritize interoperability: Choose robots that integrate with your existing software (e.g., PLCs, MES, ERP systems).
• Plan for obsolescence: Robots last 10–15 years, but software and sensors may need upgrades sooner. Budget for modular components.

Quick Practice Scenario

Scenario: Your team is automating a quality inspection line for circuit boards. The robot needs to:
1. Pick a board from a conveyor.
2. Scan it with a camera to detect defects.
3. Sort it into "pass" or "fail" bins.

Question: What sensors and actuators would you select, and why?

Answer: - Sensors: A high-resolution camera (for defect detection) + proximity sensors (to confirm board presence). - Actuators: A servo motor (for precise arm movements) + a pneumatic gripper (for gentle handling). Explanation: The camera provides visual data for defect detection, while the gripper ensures the board isn’t damaged during handling.

Last-Minute Cram Sheet

1. Robot = Sense + Plan + Act (the core loop).
2. Degrees of Freedom (DoF): More DoF = more flexibility but harder to control.
3. Actuators convert energy to motion (e.g., motors, cylinders).
4. Sensors gather data (e.g., cameras, LiDAR, force sensors).
5. End effector = the robot’s "hand" (gripper, welder, etc.).
6. Autonomy levels: Teleoperated-Semi-autonomous-Fully autonomous.
7. Kinematics: Forward (joints-position) vs. inverse (position-joints).
8. Feedback loop = real-time adjustments (e.g., grip force correction).
9. Not all robots are AI-powered—most are pre-programmed.
10. Safety first: Always follow ISO/ANSI standards for industrial robots.