Robotics and Autonomous Systems
Description
Integration of AI with robotics including motion planning, control systems, sensor integration, and autonomous decision-making for robotic applications.
Learning Objectives
Learners will understand robotics fundamentals including kinematics and dynamics, implement motion planning and path planning algorithms, integrate sensors and actuators for robotic control, develop autonomous navigation and mapping systems, and design intelligent robotic systems for various applications including industrial automation and service robotics.
Topics (7)
Human-robot interaction including natural language interfaces, gesture recognition, social robotics, collaborative robot design, safety systems, and ethical considerations in human-robot collaboration.
Foundation concepts in robotics including robot anatomy, degrees of freedom, coordinate transformations, forward and inverse kinematics, Jacobian matrices, and mathematical modeling of robotic systems.
Motion planning techniques including configuration space, path planning algorithms (A*, Dijkstra, RRT, PRM), obstacle avoidance, trajectory optimization, and real-time motion planning for dynamic environments.
Sensor integration techniques including camera systems, LiDAR processing, IMU data fusion, tactile sensing, sensor calibration, and multi-modal sensor fusion for comprehensive robotic perception.
Autonomous navigation including SLAM algorithms (EKF-SLAM, FastSLAM, GraphSLAM), localization techniques, mapping algorithms, and navigation in unknown environments for mobile robotic systems.
Robot control systems including PID control, adaptive control, robust control, actuator technologies (servo motors, stepper motors, pneumatic systems), and control system design for robotic applications.
Applications of robotics in industrial automation including manufacturing robots, assembly line automation, quality control systems, and service robotics applications in healthcare, hospitality, and domestic environments.