Syllabus

Introduction to robotics and basic mathematical tools[1+2+2]
Basic Descriptions and definitions, a brief recap of the history of robotic developments, diverse applications of robots, control challenges that may occur during such applications, different types of control paradigm required to address the relevant issues. Scope of advancement including application of AI.
Descriptions: positions, orientations, and frames, mappings: changing descriptions from frame to frame, operators: translations, rotations, and transformations, summary of interpretations, transformation arithmetic, transform equations, more on representation of orientation, transformation of free vectors, computational considerations.
Representing Positions, Representing Rotations, Rotation in the plane, Rotations in three dimensions, Rotational Transformations, Similarity Transformations, Composition of Rotations, Rotation with respect to the current frame, Rotation with respect to the fixed frame, Parameterizations of Rotations, Euler Angles, Roll, Pitch, Yaw Angles, Axis/Angle Representation, Rigid Motions, Homogeneous Transformations.

Kinematics[2+2+2]
Forward and inverse kinematics
Kinematic chains, forward kinematics: the Denavit-Hartenberg convention, existence and uniqueness issues, assigning the coordinate frames, inverse kinematics, the general inverse kinematics problem, kinematic decoupling, inverse position: a geometric approach, inverse orientation

Velocity Kinematic, Jacobians, and static forces
Notation for time-varying position and orientation, linear and rotational velocity of rigid bodies, motion of the links of a robot, velocity "propagation" from link to link, Jacobians, singularities, static forces in manipulators, Jacobians in the force domain, cartesian transformation of velocities and static forces

Mobile Robot Kinematics
Types of wheels, types of actuation, integrability of the robot motion constraints

Path and trajectory planning[4]
Configuration space, path planning using configuration space potential fields, the attractive field, the repulsive field, gradient descent planning, planning using workspace potential fields, defining workspace potential fields, mapping workspace forces to joint forces and torques, motion planning algorithm, using random motions to escape local minima, probabilistic roadmap methods, sampling the configuration space, connecting pairs of configurations, enhancement, path smoothing, trajectory planning, trajectories for point to point motion, trajectories for paths specified by via points, trajectory planning for nonholonomic mobile robots.

Mid-Semester

Dynamics[4]
Acceleration of a rigid body, mass distribution, newton's equation, Euler’s equation, iterative newton—Euler dynamic formulation, iterative vs. Closed form, an example of closed-form dynamic equations, the structure of a manipulator's dynamic equations, Lagrangian formulation of manipulator dynamics, formulating manipulator dynamics in cartesian space, inclusion of nonholonomic velocity constraints in the dynamic equation.

Control issues and paradigms[8]
actuator dynamics, set-point tracking, pd compensator, performance of PD compensators, PID compensator, saturation, feedforward control and computed torque, drive train dynamics, state space design, state feedback compensator, PD control revisited, inverse dynamics control, task space inverse dynamics, feedback linearization, constructive approaches, passivity based control, coordinate frames and constraints, natural and artificial constraints, network models and impedance, impedance operators, classification of impedance operators, Thevenin and Norton equivalents impedance model, task space dynamics and control, static force/torque relationships, task space dynamics, impedance control, hybrid impedance control, control of the differential drive mobile robot- Lyapunov based approach, backstepping kinematic into dynamics.

Issue of AI in robotic control [3]
Different advanced applications of AI in the field of trajectory planning and control, learning control laws with dynamical systems, learning how to modulate a dynamical systems, Important case studies: RNN based trajectory control for manipulators with uncertain kinematic parameters, RNN based adaptive compliance control for robots with model uncertainty, motion-force control of redundant manipulators with optimal joint torque.

The purpose of this course is to provide a basics introduction to modelling and control of robot systems. An attempt will be there to explore the interplay between control and robotics through introducing theory and demonstrating applications. It aims to provide an in-depth coverage of control design for robotic manipulators and mobile robots. The primary objective is on fundamental theory to understand the mathematical model of the robot and their control challenges. Consequently, relevant control laws and their application on practical robotic systems will be discussed. Topics may include modelling of robotic systems, linear/nonlinear control of robotic systems, control of under-actuated robotic systems, optimal control, adaptive control, behaviour-based robots. In addition, this course will also explore areas where further improvement could be achieved with aid of artificial intelligence.

Perform important linear algebraic and trigonometric manipulation for localization of a moving object.

Analyse the kinematics equations for a given robotic structure.

Analyse the complex motion profile (includes both translational and rotational motion) for a robotic system.

Plan an optimal path for robot navigation

Formulate equation of motion for a system subjected to different types of actuating and restraining torques.

Devise a control law to execute proper robotic operation.

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