Time-Optimal Trajectory Planning for Redundant Robots [recurso electrónico] : Joint Space Decomposition for Redundancy Resolution in Non-Linear Optimization / by Alexander Reiter.

NURBS Curves -- Modeling: Kinematics and Dynamics of Redundant Robots -- Approaches to Minimum-Time Trajectory Planning -- Joint Space Decomposition Approach -- Examples for Applications of Robots.

This master´s thesis presents a novel approach to finding trajectories with minimal end time for kinematically redundant manipulators. Emphasis is given to a general applicability of the developed method to industrial tasks such as gluing or welding. Minimum-time trajectories may yield economic advantages as a shorter trajectory duration results in a lower task cycle time. Whereas kinematically redundant manipulators possess increased dexterity, compared to conventional non-redundant manipulators, their inverse kinematics is not unique and requires further treatment. In this work a joint space decomposition approach is introduced that takes advantage of the closed form inverse kinematics solution of non-redundant robots. Kinematic redundancy can be fully exploited to achieve minimum-time trajectories for prescribed end-effector paths. Contents NURBS Curves Modeling: Kinematics and Dynamics of Redundant Robots Approaches to Minimum-Time Trajectory Planning Joint Space Decomposition Approach Examples for Applications of Robots Target Groups Lecturers and Students of Robotics and Automation Industrial Developers of Trajectory Planning Algorithms The Author Alexander Reiter is a Senior Scientist at the Institute of Robotics of the Johannes Kepler University Linz in Austria. His major fields of research are kinematics, dynamics, and trajectory planning for kinematically redundant serial robots.