Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms

Abstract

Abstract-A desirable property of path planning for robotic manipulation is the ability to identify solutions in a sufficiently short amount of time to be usable. This is particularly challenging for the manipulation problem due to the need to plan over high-dimensional configuration spaces and to perform computationally expensive collision checking procedures. Consequently, existing planners take steps to achieve desired solution times at the cost of low quality solutions. This paper presents a planning algorithm that overcomes these difficulties by augmenting the asymptotically-optimal RRT * with a sparse sampling procedure. With the addition of a collision checking procedure that leverages memoization, this approach has the benefit that it quickly identifies low-cost feasible trajectories and takes advantage of subsequent computation time to refine the solution towards an optimal one. We evaluate the algorithm through a series of Monte Carlo simulations of seven, twelve, and fourteen degree of freedom manipulation planning problems in a realistic simulation environment. The results indicate that the proposed approach provides significant improvements in the quality of both the initial solution and the final path, while incurring almost no computational overhead compared to the RRT algorithm. We conclude with a demonstration of our algorithm for single-arm and dual-arm planning on Willow Garage's PR2 robot.

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

“…At the core of this line of work are the PRM* and RRT* [29]. The RRT* handles any-time applications [30] and manipulators [50]. The RRT* paths can be composed of many more nodes than is strictly necessary.…”

Potential functions based sampling heuristic for optimal path planning

“…Algorithm 1 is slightly modified implementation of RRT*. In this modification, improvements were made to original algorithm in order to enhance computational efficiency of RRT* by reducing the number of calls to its collision checking procedure [19]. Following is a brief description of the main processes involved in its execution:…”

Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments

“…Algorithm 1 is a slightly modified implementation of RRT*. In this version, improvements were made to the original algorithm in order to enhance the computational efficiency of RRT* by reducing the number of calls to the ObstacleFree procedure [29]. Following are some of the processes employed by RRT*:…”

“…It then continues to refine this initial path in successive iterations, eventually returning an optimal or near optimal path towards the goal as the number of iterations approach infinity [12]. This additional guarantee of optimality makes the RRT* algorithm very useful for real-time applications [29]. However, some major constraints still exist in this RRT variant which are presented in this paper.…”