Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms
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Teller_Asymptotically-optimal.pdf
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Author(s)
Perez, Alejandro Tomas
Karaman, Sertac
Shkolnik, Alexander C.
Frazzoli, Emilio
Teller, Seth
Walter, Matthew R.
Date Issued
December 2011
Journal
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Perez, Alejandro et al. “Asymptotically-optimal Path Planning for Manipulation Using Incremental Sampling-based Algorithms.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011. 4307–4313.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/IROS.2011.6048640
