RRS: Rapidly-Exploring Random Snakes a New Method for Mobile Robot Path Planning

Recently, sampling-based path planning algorithms have been implemented in many practical robotics tasks. However, little improvements have been dedicated to the returned solution (quality) and sampling process. The aim of this paper is to introduce a new technique that improves the classical rapidly-exploring random trees (RRT) algorithm. First, the sampling step is modified in order to increase the number of possible solutions in the free space. Second, within the possible solutions, we apply an optimization scheme that gives the best solution in term of safety and shortness. The proposed solution, namely, rapidly-exploring random snakes (RRS) is a combination of a modified deformable Active Contours Model (called Snakes) and the RRT. The RRS takes the advantage of both RRT and deformable Snakes contours, respectively, in: rapidly searching new candidate nodes in the free space and circumnavigating obstacles by calculating a safe sub-path in the free space toward the new node created by the RRT. In comparison to the classical RRT, the proposed algorithm increases the probability of completeness, accelerates the convergence and generates a much safer and shorter open-loop solution, hence, increasing considerably the efficiency of the classical RRT. The proposed approach has been validated via numerical simulations and experimental results with a mobile robot.