A Expected-time Optimal Path Planning Method for Robot Target Search in Uncertain Environments

This paper presents a probabilistic path planning method for robot target search to reduce the expected-time cost in uncertain environments. Considering the validity of the manual setting probability decreases with time, a model of attenuation and growth is constructed to update the probability information of observation points. Because different direction may lead to different expected-time in the same loop, a direction choosing method is used to improve the performance of this planning method. Then, a double-level planning strategy is designed. At the top level, a heuristic sequence planning algorithm is employed to generate the sequence of observation points. At the lower level, the Artificial Potential Field (APF) is applied to plan the optimal path between every two observation points. Simulations demonstrated that this method can reduce the expected-time in repeated target search tasks by increasing a little computational cost.