A universal index and an improved PSO algorithm for optimal pose selection in kinematic calibration of a novel surgical robot

In this paper, a universal observability index is put forward for optimal measurement configuration selection in robot kinematic calibration experiments. The effectiveness of two new criteria based on the universal observability index can be verified by the simulation and experiments on a serial surgical robot. For maximizing the universal observability index, an improved and a classical Particle Swarm Optimization approaches are proposed and utilized firstly for optimal pose selection respectively. Simulation and experimental results illustrate the feasibility and benefits of the proposed searching approaches respectively. Additionally, it should be pointed out that two significant situations, namely, row-switching of the unknown parameters vector and exchanging different configurations of the same particle, may be commonly encountered during the searching procedure. Fortunately, it can be demonstrated that every observability index would be invariable and efficient still with respect to the above two scenarios.