Advanced Certificate in Robotics: Motion Planning & Control
-- ViewingNowThe Advanced Certificate in Robotics: Motion Planning & Control is a comprehensive course that equips learners with essential skills in robotics, a rapidly growing field with high industry demand. This program covers critical topics such as kinematics, dynamics, motion planning, and control, providing a deep understanding of how robots move and interact with their environment.
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⢠Motion Planning Algorithms: an in-depth study of various motion planning algorithms, including Rapidly-exploring Random Trees (RRT), Dijkstra's, A\*, and Dynamic Programming. This unit will also cover the optimization of these algorithms for real-world applications.
⢠Kinematics and Dynamics: an exploration of the principles of kinematics and dynamics in robotics, including forward and inverse kinematics, and the dynamics of robotic manipulators. This unit will focus on the application of these principles in the design of motion control systems.
⢠Control Systems Theory: an introduction to control systems theory, including the design and analysis of feedback control systems, state-space representation, and modern control theory. This unit will cover the application of control systems theory in robotics.
⢠Robot Sensors and Perception: an examination of the various sensors used in robotics, including vision sensors, tactile sensors, and range finders. This unit will also cover sensor data fusion, probabilistic robotics, and the use of sensor data for motion planning and control.
⢠Robot Manipulation and Grasping: an exploration of the principles of robot manipulation and grasping, including the design of grasping algorithms, manipulation planning, and force control. This unit will cover the application of these principles in manufacturing, healthcare, and other industries.
⢠Mobile Robot Navigation: an in-depth study of the principles of mobile robot navigation, including SLAM (Simultaneous Localization and Mapping), odometry, and sensor-based navigation. This unit will cover the application of these principles in various industries, such as agriculture, mining, and logistics.
⢠Machine Learning for Robotics: an introduction to machine learning techniques and their application in robotics, including supervised and unsupervised learning, reinforcement learning, and deep learning. This unit will cover the implementation of these techniques in motion planning, control, and perception.
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