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Target search path planning for mobile robot under fixed time

In the target search problem of mobile robot in uncertain environment, the probability of target discovery at the observation point is often set as an ideal uniform distribution, and its path optimization index is usually the shortest distance, but the shortest distance path is not equal to the optimal expected time path To solve this problem, a probabilistic multi-objective search algorithm based on expected time is proposed in this paper In view of the phenomenon that different access order of observation points will lead to different expected time, a hierarchical path optimization strategy is adopted Firstly, a new model for calculating the probability of non-uniform target distribution is constructed; Then, in the upper level sequence planning, the improved improved circle algorithm is used to generate the expected observation sequence; Finally, in the feasible path planning between observation points of the lower feature map, the improved fast random spanning tree algorithm (GBC - RRT) is used The experimental results show that the proposed method can significantly shorten the expected time of mobile robot target search, and can obtain the search path of the optimal expected time in the workspace with uncertain and non-uniform distribution of targets.