🦀Robotics and Bioinspired Systems Unit 1 – Robotics Fundamentals

Key Concepts and Terminology

• Robotics involves the design, construction, operation, and application of robots, as well as computer systems for their control, sensory feedback, and information processing
• Key terms include actuators (motors, pneumatics, hydraulics), sensors (encoders, cameras, LiDARs), end effectors (grippers, tools), and control systems (microcontrollers, PLCs)
• Degrees of freedom (DOF) refer to the number of independent parameters that define the configuration of a robotic system
  • Each joint in a robotic arm represents one DOF
  • 6 DOF are required for a robot to reach any position and orientation in 3D space
• Forward kinematics determines the position and orientation of the end effector given the joint angles or positions
• Inverse kinematics calculates the joint angles or positions required to achieve a desired end effector position and orientation
• Robot autonomy describes a robot's ability to perform tasks with minimal human intervention, ranging from teleoperation to fully autonomous systems
• Proprioception is a robot's ability to sense its own position, orientation, and movement, typically using encoders and inertial measurement units (IMUs)

Robot Components and Structure

• Robots consist of a mechanical structure, actuators, sensors, and a control system that work together to perform tasks
• The mechanical structure provides support and determines the robot's shape, size, and workspace
  • Common configurations include cartesian (gantry), cylindrical, spherical, and articulated (anthropomorphic) robots
• Actuators generate motion and force, enabling the robot to move and interact with its environment
  • Electric motors (DC, servo, stepper) are widely used due to their precision and controllability
  • Pneumatic and hydraulic actuators offer high power-to-weight ratios but are less precise
• Sensors allow the robot to gather information about its internal state and external environment
  • Encoders measure joint positions and velocities for precise motion control
  • Cameras and LiDARs provide visual and depth data for object recognition and navigation
• End effectors are the tools or devices attached to the robot's arm, designed for specific tasks (gripping, welding, painting)
• The control system processes sensor data, executes algorithms, and generates commands for the actuators to achieve desired behaviors

Sensors and Actuators

• Sensors are essential for robots to perceive their environment and internal state, enabling them to interact with the world and complete tasks effectively
• Proprioceptive sensors measure the robot's internal state, such as joint positions (encoders), velocities (tachometers), and forces/torques (load cells)
  • Encoders are widely used for precise motion control, providing feedback on the angular position and velocity of motor shafts or joints
• Exteroceptive sensors gather information about the robot's environment, such as visual data (cameras), distances (ultrasonic, IR), and object properties (tactile, force/torque)
  • Cameras capture visual information, enabling object recognition, tracking, and visual servoing
  • LiDARs provide 3D point clouds for mapping, localization, and obstacle avoidance
• Sensor fusion combines data from multiple sensors to improve accuracy, reliability, and robustness
  • Kalman filters are commonly used to estimate the robot's state by fusing data from encoders, IMUs, and GPS
• Actuators convert energy into motion, allowing robots to move and apply forces to their environment
• Electric motors are the most common type of actuator, offering precise control and high efficiency
  • DC motors provide continuous rotation and are often used with gearboxes for increased torque
  • Servo motors incorporate feedback control for precise position and velocity control
  • Stepper motors enable precise incremental motion without the need for feedback
• Pneumatic and hydraulic actuators use compressed air or fluid to generate linear or rotary motion, providing high force output but lower precision compared to electric motors

Control Systems and Programming

• Control systems are responsible for processing sensor data, executing algorithms, and generating commands for actuators to achieve desired robot behaviors
• Microcontrollers (Arduino, Raspberry Pi) are commonly used for low-level control, offering flexibility and ease of programming
  • They interface with sensors and actuators, execute control loops, and communicate with higher-level systems
• Programmable Logic Controllers (PLCs) are robust industrial control systems used in manufacturing and automation
  • They provide reliable real-time control and are programmed using ladder logic or other IEC 61131-3 languages
• Robot Operating System (ROS) is a popular open-source framework for robot software development
  • It provides a modular architecture, communication protocols, and libraries for common robotics tasks (navigation, manipulation)
  • ROS enables the integration of various programming languages (C++, Python) and tools (Gazebo simulator, MoveIt motion planning)
• Motion control involves the planning and execution of robot movements to achieve desired trajectories
  • PID (Proportional-Integral-Derivative) control is a widely used feedback control algorithm for precise motion control
  • Trajectory planning generates smooth, feasible paths between start and goal configurations while avoiding obstacles
• Machine learning and artificial intelligence techniques are increasingly used in robotics for perception, decision-making, and adaptation
  • Deep learning enables robots to learn from large datasets and improve their performance over time (object recognition, grasping)
  • Reinforcement learning allows robots to learn optimal control policies through trial-and-error interaction with their environment

Kinematics and Motion Planning

• Kinematics is the study of robot motion without considering the forces that cause it, focusing on the relationship between joint positions and end effector pose
• Forward kinematics determines the position and orientation of the end effector given the joint angles or positions
  • It uses the Denavit-Hartenberg (DH) convention to systematically assign coordinate frames to each joint and link
  • The forward kinematics equation is a series of matrix multiplications: $T_{end}^{base} = T_1^{base} \cdot T_2^1 \cdot ... \cdot T_{end}^{n-1}$
• Inverse kinematics calculates the joint angles or positions required to achieve a desired end effector position and orientation
  • It is more complex than forward kinematics due to multiple solutions (redundancy) and singularities
  • Numerical methods (Jacobian pseudoinverse, cyclic coordinate descent) are used to solve the inverse kinematics problem iteratively
• Velocity kinematics relates joint velocities to end effector linear and angular velocities using the Jacobian matrix
  • The Jacobian matrix $J(q)$ is a function of the joint angles and maps joint velocities to end effector velocities: $\dot{x} = J(q) \cdot \dot{q}$
• Motion planning generates feasible trajectories for the robot to move from a start to a goal configuration while avoiding obstacles
  • Sampling-based methods (RRT, PRM) explore the configuration space by randomly sampling points and connecting them to form a graph
  • Optimization-based methods (CHOMP, TrajOpt) formulate motion planning as an optimization problem, minimizing a cost function that considers obstacle avoidance and smoothness
• Motion primitives are pre-defined, parameterized trajectories that can be combined to create complex behaviors
  • They simplify motion planning by reducing the search space and enabling the use of libraries of common movements (pick and place, reaching)

Robot Types and Applications

• Industrial robots are used in manufacturing for tasks such as assembly, welding, painting, and material handling
  • They are typically large, high-payload robots with 6 or more DOF, designed for speed, accuracy, and repeatability
  • Examples include KUKA, ABB, and Fanuc robots used in automotive and electronics manufacturing
• Service robots assist humans in various tasks, such as household chores, healthcare, and education
  • They are designed to interact safely with humans and adapt to unstructured environments
  • Examples include the iRobot Roomba vacuum cleaner, the Softbank Pepper humanoid robot, and the da Vinci surgical system
• Mobile robots are designed to navigate and operate in various environments, such as indoor, outdoor, aerial, and underwater
  • They use sensors (cameras, LiDARs, IMUs) and algorithms (SLAM, path planning) to perceive and navigate their surroundings
  • Examples include the Mars rovers (Curiosity, Perseverance), the Boston Dynamics Spot quadruped, and the DJI Mavic drones
• Collaborative robots (cobots) are designed to work safely alongside humans in shared workspaces
  • They feature force-limiting actuators, soft materials, and advanced sensors to detect and avoid collisions with humans
  • Examples include the Universal Robots UR series and the Rethink Robotics Sawyer robot
• Soft robots are made of compliant materials that allow them to adapt to their environment and interact safely with objects
  • They are inspired by biological systems and offer advantages such as flexibility, durability, and conformability
  • Examples include the Harvard Octobot, the MIT Cheetah, and the Stanford OceanOne underwater humanoid

Challenges and Future Trends

• Robotic perception remains a significant challenge, especially in unstructured and dynamic environments
  • Advances in computer vision, deep learning, and sensor fusion are improving robots' ability to understand and interact with their surroundings
  • Future robots will need to reliably recognize objects, interpret scenes, and adapt to changing conditions
• Human-robot interaction is crucial for the successful integration of robots into society
  • Developing intuitive interfaces, natural communication, and trust between humans and robots is an active area of research
  • Future robots will need to understand human intentions, emotions, and social cues to collaborate effectively
• Robotic manipulation is challenging due to the complexity of grasping and manipulating objects with varying shapes, sizes, and materials
  • Advances in soft robotics, tactile sensing, and learning-based methods are enabling more dexterous and adaptable manipulation
  • Future robots will need to autonomously grasp and manipulate objects in unstructured environments, such as homes and workplaces
• Autonomous navigation and decision-making are essential for robots to operate in complex, dynamic environments
  • Advances in simultaneous localization and mapping (SLAM), motion planning, and reinforcement learning are enabling more robust and adaptive navigation
  • Future robots will need to make intelligent decisions under uncertainty, considering multiple objectives and constraints
• Robotic swarms and multi-robot systems offer the potential for increased efficiency, flexibility, and robustness
  • Coordinating and controlling large numbers of robots poses challenges in communication, task allocation, and emergent behavior
  • Future robot swarms could be used for applications such as search and rescue, environmental monitoring, and space exploration

Hands-On Projects and Labs

• Building a simple robot arm using servo motors and an Arduino microcontroller
  • Students learn about forward and inverse kinematics, servo control, and Arduino programming
  • The project involves designing the arm structure, wiring the servos, and implementing control algorithms to achieve desired end effector positions
• Developing a mobile robot for maze navigation using ROS and a Turtlebot platform
  • Students learn about ROS architecture, sensor integration, and navigation algorithms (A*, RRT)
  • The project involves configuring the Turtlebot sensors (LiDAR, camera), implementing navigation nodes, and testing the robot in a maze environment
• Implementing a vision-based object tracking system using OpenCV and a webcam
  • Students learn about computer vision techniques (color filtering, contour detection) and proportional control
  • The project involves detecting and tracking a colored object, calculating its centroid, and controlling a pan-tilt mechanism to keep the object centered in the camera frame
• Designing and simulating a robotic gripper using CAD software and finite element analysis
  • Students learn about gripper design principles, 3D modeling, and stress analysis
  • The project involves designing a gripper for a specific task (e.g., picking up a can), simulating its performance under load, and optimizing the design for strength and weight
• Conducting experiments with a collaborative robot (e.g., Universal Robots) to study human-robot interaction
  • Students learn about cobot safety features, force control, and user interface design
  • The project involves programming the cobot to perform a collaborative task (e.g., handover), designing a user interface, and conducting user studies to evaluate the effectiveness of the interaction