Planning of Workplaces with Multiple Kinematically Redundant Robots (2007)

This thesis provides new methods for planning and optimization of robotic workplaces,&#13;\ni.e. workplaces with assistance of a robotic system. The robots may work autonomously&#13;\nor in cooperation with man. The thesis covers cooperation between multiple robots and&#13;\nlays stress on robots with kinematic redundancy. As highly demanding application area,&#13;\noptimal design and preoperative planning for minimally invasive and open robotic surgery&#13;\nis chosen.&#13;\nOptimization and planning of robotic workplaces are important instruments to cope with&#13;\nthe increased complexity of today’s robotic applications and to ensure safe operation.&#13;\nAlgorithms and devices to facilitate and partially automatize planning and optimization&#13;\nare, however, barely existent so far – with currently available tools the user mostly has to&#13;\nresort to a trial and error approach. Especially for complicated tasks requiring e.g. several&#13;\nrobots cooperating in an unstructured environment, it is very improbable that a good (if&#13;\nat all sufficient) setup of the robotic workplace can be found this way. Therefore, the&#13;\ninclusion of algorithms to automatize the planning procedure as presented in this thesis&#13;\nis the evident next step to be taken.&#13;\nClosed form solutions for inverse kinematics and singularities provide the core of a reliable&#13;\nworkplace optimization system and are developed in this thesis for serial kinematically&#13;\nredundant robots. Unlike state of the art methods, the presented inverse kinematics computation&#13;\ndoes not suffer from algorithmic singularities. The thesis describes an accordingly&#13;\ndeveloped software library for inverse kinematics and shows both a planning procedure&#13;\ninvolving the medical robot KineMedic, and a real-time application, providing inverse&#13;\nkinematics for Cartesian control of the robotic system Justin with a computation time of&#13;\nlower than 0.6 ms for all 18 considered joints.&#13;\nBased on the closed form solutions, the thesis presents a complete procedure for workplace&#13;\noptimization and robot synthesis that uses a two-step algorithm based on Genetic Algorithms&#13;\nand a subsequent Sequential Quadratic Programming method. The thesis develops&#13;\nseveral optimization criteria and demonstrates the performance of the methods with a&#13;\npreoperative planning procedure as well as with the kinematic design optimization of the&#13;\nKineMedic system.&#13;\nThe algorithms implemented in this thesis help the human during the decision taking&#13;\nprocedure, by e.g. providing a preselection of (according to the chosen criteria) good&#13;\nsolutions or by carrying out an optimization in a certain subspace while leaving the determination&#13;\nof the remaining parameters to the human. The thesis facilitates the transfer&#13;\nof a chosen setup into the real environment using a handheld contact-free surface-based&#13;\nregistration procedure with an overall worst case error of 3 mm and a new handheld device&#13;\nto automatically project relevant structures into the real environment.