Adaptive sampling for generalized probabilistic roadmaps

Abstract

In this paper, an adaptive sampling strategy is presented for the generalized sampling-based motion planner, 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.

Resolution Independent Density Estimation for motion planning in high-dimensional spaces

“…In the most general cases, where the configuration space is complex or the workspace is implicitly defined, adaptive sampling can be used to sample new points based only on information related to previously sampled nodes. Examples of adaptive sampling can be found in Visibility PRM [18], Cross-entropy motion planning [19], GPRM [20] and Instance-based Learning [2 1], among others.…”

Probabilistic Roadmaps Based Motion Planning of a 4 Degree of Freedom Robot

“…In the field of solving a high DOF robot's motion planning, there is only a few effective methods. Probabilistic roadmaps method (PRM) is one of such an effective and efficient method [1][2][3][4]. PRM proceeds in two phases: a learning phase and a query phase.…”