Abstract
Computation of a collision-free path for a movable object among obstacles is an important problem in the fields of robotics, CIM and AI. Various automatic task level programming systems can be build for robot guidance, teleoperation, assembly and disassembly among others, if a suitable method for motion planning is available. In the basic variation of motion planning, the task is to generate a collision-free path for a movable object among known and static obstacles. Classically the problem was defined for a rigid 6 degrees-of-freedom body as ‘the piano mover's problem’. However, the majority of the research has been conducted in the field of robotics, often under the title of path planning. Rapidly-Exploring Random Trees (RRTs) are a recently developed representation on which fast continuous domain path planners can be based. In this work, we have built a parallel path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our distributed algorithm, PRRT (parallel RRT), introduces a parallel extension of previous RRT work, the process splitting and parallel cost penalty search with a comment on Real Time Stagnancy reduction, which improves re-planning efficiency, decreases latency involved in finding feasible paths and the quality of generated paths. PRRT is successfully applied to a real-time multi-robot system. In this paper we illustrate how it is possible to implement a parallel version of RRT based motion planner which yields optimal speed up.
Highlights
Path planning has been a much studied problem over the past two decades, whose appeal stems from its applicability to many diverse areas spanning industrial robot locomotion, autonomous actors in computer animation, protein folding, drug design etc.in complicated, fast evolving environments such as Robocup (Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., and Osawa, E., 1995), currently popular approaches to path-planning have their strengths, but still leave much to be desired
One of the recently developed tools that may help tackle the problem of realtime path planning are Rapidly-exploring random trees (RRTs) (LaValle,.M.,1998).RRTs employ randomization to explore large state spaces efficiently, and can develop the basis for a probabilistically complete though non-optimal Kino dynamic path planner (LaValle,S.M. and Kuffner,J., 2001)
The distributed RRT planner we developed is roughly competitive with these other methods in that both can meet tight timing requirements and can reuse information from previous plans, but at this point it does not perform significantly as expected with the parallel paradigms being implanted the base RRT system is relatively easy to extend to environments with moving obstacles, higher dimensional state spaces, and kinematics constraints
Summary
Path planning has been a much studied problem over the past two decades, whose appeal stems from its applicability to many diverse areas spanning industrial robot locomotion, autonomous actors in computer animation, protein folding, drug design etc.in complicated, fast evolving environments such as Robocup (Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., and Osawa, E., 1995), currently popular approaches to path-planning have their strengths, but still leave much to be desired. One of the recently developed tools that may help tackle the problem of realtime path planning are Rapidly-exploring random trees (RRTs) (LaValle,.M.,1998).RRTs employ randomization to explore large state spaces efficiently, and can develop the basis for a probabilistically complete though non-optimal Kino dynamic path planner (LaValle,S.M. and Kuffner,J., 2001) Their strengths are that they can efficiently find paths in high dimensional spaces because they avoid the state explosion that discretizes faces. One set of reactive methods that have proved quite popular are potential fields and motor schemas (Arkin, R.C., 1989) They meet the need for action under time constraints, these methods suffer from the lack of look ahead, which can lead to highly non-optimal paths and problems with oscillation. This is commonly accepted, and dealt with at a higher layer of the system that detects failure or a local minimum and tries to break out of it
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