AI Notes
Master robotics basics. Robot control, sensing. Robotics 2024.
Introduction
Robotics is the branch of artificial intelligence that deals with the design, construction, operation, and application of machines capable of carrying out complex tasks in the physical world. A robot is fundamentally an intelligent agent that perceives its environment through sensors, reasons about what actions to take, and acts upon the environment through actuators. The field sits at the intersection of mechanical engineering, electrical engineering, computer science, and artificial intelligence.
What distinguishes a robot from a simple automated machine is intelligence: the ability to adapt to changing conditions, make decisions under uncertainty, and perform tasks that were not explicitly pre-programmed. A washing machine follows a fixed program; a robot vacuum navigates around furniture it has never seen before.
Components of a Robotic System
Sensors (Perception)
Sensors provide the robot with information about its environment and its own state:
- Cameras: RGB images, depth maps (stereo or structured light)
- LIDAR: 3D point clouds using laser scanning (360-degree maps)
- IMU (Inertial Measurement Unit): Acceleration and rotation rate
- Encoders: Precise joint angles and wheel rotations
- Force/Torque sensors: Measure contact forces for manipulation
- Ultrasonic: Distance measurement using sound waves
Actuators (Action)
Actuators convert electrical signals into physical motion:
- DC Motors: Continuous rotation (wheels, conveyors)
- Servo Motors: Precise angular positioning (robot arms)
- Stepper Motors: Discrete steps of rotation (3D printers)
- Hydraulic/Pneumatic: High force applications (industrial robots)
- Linear actuators: Push/pull motion (grippers)
Robot Classifications
By Workspace
- Fixed base (industrial arms): Work within reach envelope
- Mobile (wheeled/legged): Navigate through environment
- Aerial (drones): Fly in 3D space
- Underwater (AUVs): Operate in aquatic environments
By Degrees of Freedom (DOF)
- 2-DOF: Planar movement (SCARA robots for pick-and-place)
- 3-DOF: 3D positioning (Delta robots for packaging)
- 6-DOF: Full position + orientation (most industrial arms)
- 7+ DOF: Redundant (more flexible, can avoid obstacles)
By Intelligence Level
- Pre-programmed: Follow fixed paths (early industrial robots)
- Reactive: Respond to sensor inputs (Roomba obstacle avoidance)
- Deliberative: Plan ahead (self-driving cars)
- Learning: Improve with experience (RL-trained manipulation)
The Sense-Plan-Act Cycle
Every intelligent robot operates on a continuous loop:
| 1. SENSE: Read all sensors | build world model |
| - Camera image | object detection → "cup at position (0.3, 0.5, 0.1)" |
| - LIDAR scan | obstacle map → "wall 2m ahead" |
| - Encoders | joint angles → "arm at configuration [30°, 45°, 60°, ...]" |
| 2. PLAN | Given current state and goal, find action sequence |
| - Path planning | "Move from A to B avoiding obstacles" |
| - Task planning | "To make coffee: grab cup, move to machine, press button" |
| - Motion planning | "Joint trajectory to reach target pose" |
| 3. ACT | Execute planned actions through actuators |
Real-World Example: Pick-and-Place Robot
Consider a robot arm that must pick up objects from a conveyor belt and sort them into bins:
Perception
Camera detects object → CNN classifies as "red apple"
Depth sensor measures position → (x=0.4m, y=0.2m, z=0.05m)
Planning
Grasp planner → approach from above, close gripper at 0.05m width
Motion planner → compute joint trajectory avoiding collisions
Sort decision → "red apple" → bin 3
Execution
Move arm to pre-grasp position (above object)
Lower arm to grasp position
Close gripper with appropriate force
Lift object
Move to bin 3
Release object
Return to home position
Control loop: PID controller maintains trajectory at 1000 Hz
error = desired_position - actual_position
command = Kp×error + Ki×∫error + Kd×(d/dt)error
Kinematics: The Mathematics of Robot Motion
Forward kinematics computes end-effector position from joint angles. Inverse kinematics finds joint angles for a desired end-effector position:
| Joint angles | θ1 = 30°, θ2 = 45° |
| Link lengths | L1 = 1m, L2 = 0.5m |
| Target | (0.8, 0.6) |
Key Challenges in Robotics
Uncertainty: Real sensors are noisy. A camera might misidentify an object. GPS has meter-level error. Robots must reason probabilistically.
Real-time constraints: A self-driving car traveling at 60 mph covers 27 meters per second. Decisions must be made in milliseconds.
Safety: Robots interact with humans and fragile objects. Force control, compliance, and emergency stops are critical.
Generalization: A robot trained to pick up cups may fail with bowls. Achieving human-level adaptability to novel objects and situations remains an open challenge.
Summary
Robotics combines AI reasoning with physical embodiment, requiring integration of perception, planning, and control. From industrial arms to autonomous vehicles, robots are increasingly capable of complex tasks in unstructured environments. Understanding the fundamental architecture of robotic systems, the sense-plan-act cycle, and the mathematical foundations of kinematics and control theory provides the basis for developing intelligent machines that can operate reliably in the real world.
Exam Focus
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