Knowledge-based workspace optimization of a redundant robot for minimally invasive robotic surgery (MIRS)

This work examines the problem of a redundant robot's limited workspace in the field of minimally invasive surgery. The proposed procedure to overcome difficulties with joint angle restrictions and finding suitable pivot points works as follows. At first, the target region during a operation has to be defined. An algorithm structures the information of tracked and annotated operations. Each phase of the operation is regarded separately. The result, a grid mesh, is used to test the robot performance with the restriction of the pivot point. The knowledge base contains the structured information about the target region of a phase. Different criteria are defined and evaluated to find the optimal parameter set and a pivot point. Reachability and keeping the pivot restriction are represented in these criteria. The parameter set exists because of the redundancy. Thus, an objective function is proposed to bring the different evaluation criteria together with regard to different possible parameter sets. Given these parameter sets the workspace of the robot is optimized concerning the target region of the operation.