Asymptotically-optimal Motion Planning using lower bounds on cost | IEEE Conference Publication | IEEE Xplore

Asymptotically-optimal Motion Planning using lower bounds on cost


Abstract:

Many path-finding algorithms on graphs such as A* are sped up by using a heuristic function that gives lower bounds on the cost to reach the goal. Aiming to apply similar...Show More

Abstract:

Many path-finding algorithms on graphs such as A* are sped up by using a heuristic function that gives lower bounds on the cost to reach the goal. Aiming to apply similar techniques to speed up sampling-based motion-planning algorithms, we use effective lower bounds on the cost between configurations to tightly estimate the cost-to-go. We then use these estimates in an anytime asymptotically-optimal algorithm which we call Motion Planning using Lower Bounds (MPLB). MPLB is based on the Fast Marching Trees (FMT*) algorithm [1] recently presented by Janson and Pavone. An advantage of our approach is that in many cases (especially as the number of samples grows) the weight of collision detection in the computation is almost negligible compared to the weight of nearest-neighbor queries. We prove that MPLB performs no more collision-detection calls than an anytime version of FMT*. Additionally, we demonstrate in simulations that for certain scenarios, the algorithmic tools presented here enable efficiently producing low-cost paths while spending only a small fraction of the running time on collision detection.
Date of Conference: 26-30 May 2015
Date Added to IEEE Xplore: 02 July 2015
ISBN Information:
Print ISSN: 1050-4729
Conference Location: Seattle, WA, USA

References

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