Planning of Workplaces with Multiple Kinematically Redundant Robots

Abstract

This thesis provides new methods for planning and optimization of robotic workplaces, i.e. workplaces with assistance of a robotic system. The robots may work autonomously or in cooperation with man. The thesis covers cooperation between multiple robots and lays stress on robots with kinematic redundancy. As highly demanding application area, optimal design and preoperative planning for minimally invasive and open robotic surgery is chosen. Optimization and planning of robotic workplaces are important instruments to cope with the increased complexity of today’s robotic applications and to ensure safe operation. Algorithms and devices to facilitate and partially automatize planning and optimization are, however, barely existent so far – with currently available tools the user mostly has to resort to a trial and error approach. Especially for complicated tasks requiring e.g. several robots cooperating in an unstructured environment, it is very improbable that a good (if at all sufficient) setup of the robotic workplace can be found this way. Therefore, the inclusion of algorithms to automatize the planning procedure as presented in this thesis is the evident next step to be taken. Closed form solutions for inverse kinematics and singularities provide the core of a reliable workplace optimization system and are developed in this thesis for serial kinematically redundant robots. Unlike state of the art methods, the presented inverse kinematics computation does not suffer from algorithmic singularities. The thesis describes an accordingly developed software library for inverse kinematics and shows both a planning procedure involving the medical robot KineMedic, and a real-time application, providing inverse kinematics for Cartesian control of the robotic system Justin with a computation time of lower than 0.6 ms for all 18 considered joints. Based on the closed form solutions, the thesis presents a complete procedure for workplace optimization and robot synthesis that uses a two-step algorithm based on Genetic Algorithms and a subsequent Sequential Quadratic Programming method. The thesis develops several optimization criteria and demonstrates the performance of the methods with a preoperative planning procedure as well as with the kinematic design optimization of the KineMedic system. The algorithms implemented in this thesis help the human during the decision taking procedure, by e.g. providing a preselection of (according to the chosen criteria) good solutions or by carrying out an optimization in a certain subspace while leaving the determination of the remaining parameters to the human. The thesis facilitates the transfer of a chosen setup into the real environment using a handheld contact-free surface-based registration procedure with an overall worst case error of 3 mm and a new handheld device to automatically project relevant structures into the real environment