基于GA-OCPA学习系统的无人机路径规划方法

doi:10.7527/S1000-6893.2017.321275

Abstract

Abstract:

To solve the problem of deficiency in real-timeliness and applicability of path planning for the Unmanned Aerial Vehicle (UAV) in the unknown airspace, the real-time path planning of the UAV is simulated as a self-learning behavior under the condition of external stimuli, based on the biological operant conditioning theory. The probabilistic automaton is combined with the genetic algorithm to construct a learning system of Genetic Algorithm-Operant Conditioning Probabilistic Automaton (GA-OCPA) according to the Skinner operant conditioning. The UAVs' evasion maneuvering flight speed, rolling acceleration and climbing acceleration are taken as the learning behaviors of the system, and the probability of selection and individual fitness are calculated after each learning attempt. The optimal path can then be obtained by searching for the best behavior using the genetic algorithm. The knowledge base of the best learned behaviors is established using Incremental Hierarchical Discriminant Regression (IHDR), and the matching mapping between the threat state and path planning is then formed. The result shows the viability and applicability of the GA-OCPA learning system for UAV path planning.

Key words: Unmanned Aerial Vehicle (UAV), path planning, genetic algorithm, operant conditioning, probabilistic automaton

CLC Number:

V249.1

LIU Xin, YANG Xiaopeng, LIU Yufan, YAO Kun. UAV path planning based on GA-OCPA learning system[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017, 38(11): 321275-321275.

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UAV path planning based on GA-OCPA learning system

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