Direct Policy Search with Variable-Length Genetic Algorithm for Single Beacon Cooperative Path Planning
This paper focuses on Direct Policy Search (DPS) for cooperative path planning of a single beacon vehicle supporting Autonomous Underwater Vehicles (AUVs) performing surveying missions. Due to the lack of availability of GPS signals underwater, the position errors of the AUVs grow with time even though they are equipped with proprioceptive sensors for dead reckoning. One way to minimize this error is to have a moving beacon vehicle with good positioning data transmit its position acoustically from different locations to other AUVs. When the position is received, the AUVs can fuse this data with the range measured from the travel time of acoustic transmission to better estimate their own positions and keep the error bounded. In this work, we address the beacon vehicle’s path planning problem which takes into account the position errors being accumulated by the supported survey AUVs. We represent the path planning policy as state-action mapping and employ Variable-Length Genetic Algorithm (VLGA) to automatically discover the number of representative states and their corresponding action mapping. We show the resultant planned paths using the learned policy are able to keep the position errors of the survey AUVs bounded over the mission time.
KeywordsPosition Error Path Planning Markov Decision Process Dead Reckoning Path Planning Problem
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