Comprehensive evaluation method for performance of unmanned robot applied to automotive test using fuzzy logic and evidence theory and FNN

In order to obtain reliable and exact evaluation, a new comprehensive evaluation method for performance of an unmanned robot applied to automotive test (URAT) using fuzzy logic, evidence theory and fuzzy neural network (FNN) is presented in this paper. Throttle repeatability, speed tracking accuracy, speed repeatability, driving shock degree are used as the system evaluation index. The subjective evaluation results with various expressions are quantified using fuzzy logic. The group decision making with quantified subjective evaluation results from various drivers is combined through evidence theory. The objective evaluation indexes measured by instrumentation and the corresponding combined subjective evaluation are self-learned and trained with FNN. The comprehensive performance evaluation system of the URAT is established. Finally, real vehicle experiments are conducted. The effectiveness of the presented method for the URAT is experimentally verified.