In this paper, an adaptive sampling strategy is presented for the generalized sampling-based motion plan- ner, generalized probabilistic roadmap （GPRM）. These planners are designed to account for stochastic map and ...In this paper, an adaptive sampling strategy is presented for the generalized sampling-based motion plan- ner, generalized probabilistic roadmap （GPRM）. These planners are designed to account for stochastic map and model uncertainty and provide a feedback solution to the motion planning problem. Intelligently sampling in this framework can result in large speedups when compared to naive uniform sampling. By using the information of transition probabilities, encoded in these generalized planners, the proposed strategy biases sampling to improve the efficiency of sampling, and increase the overall success probability of GPRM. The strategy is used to solve the motion planning problem of a fully actuated point robot and a 3-DOF fixed-base manipulator on several maps of varying difficulty levels, and results show that the strategy helps solve the problem efficiently, while simultaneously increasing the success probability of the solution. Results also indicate that these rewards increase with an increase in map complexity.展开更多
基金supported by the Air Force Office of Scientific Research, U.S.A. (AFOSR)
文摘In this paper, an adaptive sampling strategy is presented for the generalized sampling-based motion plan- ner, generalized probabilistic roadmap （GPRM）. These planners are designed to account for stochastic map and model uncertainty and provide a feedback solution to the motion planning problem. Intelligently sampling in this framework can result in large speedups when compared to naive uniform sampling. By using the information of transition probabilities, encoded in these generalized planners, the proposed strategy biases sampling to improve the efficiency of sampling, and increase the overall success probability of GPRM. The strategy is used to solve the motion planning problem of a fully actuated point robot and a 3-DOF fixed-base manipulator on several maps of varying difficulty levels, and results show that the strategy helps solve the problem efficiently, while simultaneously increasing the success probability of the solution. Results also indicate that these rewards increase with an increase in map complexity.
