Autonomous Navigation Trajectory Evaluation Method Based on Uncertainty Analysis
YAN Yan1,2, TANG Zhenmin2, LIU Jiayin2
1. R&D Center, China Academy of Launch Vehicle Technology, Beijing 100076, China;
2. Institute of Pattern Recognition and Artificial Intelligence, Nanjing University of Science and Technology, Nanjing 210094, China
In order to achieve qualitative and quantitative evaluation of autonomous navigation trajectory, a trajectory analysis method based on uncertainty cloud model is proposed. In this method, robot's trajectory features are extracted from autonomous driving and avoiding actions, and corresponding feature cloud models for position warp, direction warp and obstacle-avoidance safety distance trajectories are generated. In cloud model, expectation is the basic metric of trajectories, and the fuzziness and randomness of features are expressed by entropy and hyper-entropy. By taking advantages of uncertainty metric of cloud models, the transient state and stability in autonomous navigation can be calculated. Experiment results show that this method can effectively be used in trajectory evaluation of autonomous navigation, and it can compensate shortcomings of reinforcement learning methods in stability evaluation.
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