Performance Guarantees for Receding Horizon Search with Terminal Cost

Biggs, Benjamin; Stilwell, Daniel J.; Yetkin, Harun; McMahon, James

doi:10.1109/iros40897.2019.8968202

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…In prior works [12] and [13], it was shown that the appropriate use of a terminal reward when constructing receding horizon paths can guarantee a lower bound on the value of receding horizon paths produced. The primary tool in producing this lower bound is a lower bound on the value-togo or a lower bound on the optimal value of the remainder of the path normally ignored during receding horizon planning.…”

Section: Receding Horizon Path Planningmentioning

confidence: 99%

“…However, the sequence of path segments in a receding horizon implementation do not inherit the near-optimality of each individual segment. In prior work [12], [13] the authors showed how to append a specific terminal reward to the short horizon optimization problem used for each path segment such that the reward of the overall receding horizon path is guaranteed to exceed a desirable lower bound. This result was demonstrated for the explicit reward function associated with a robotic search problem in a discrete environment through numerical experiments.…”

Section: Introductionmentioning

confidence: 99%

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Experiments in Underwater Feature Tracking with Performance Guarantees Using a Small AUV

Biggs¹,

He²,

McMahon³

et al. 2022

Preprint

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We present the results of experiments performed using a small autonomous underwater vehicle to determine the location of an isobath within a bounded area. The primary contribution of this work is to implement and integrate several recent developments real-time planning for environmental mapping, and to demonstrate their utility in a challenging practical example. We model the bathymetry within the operational area using a Gaussian process and propose a reward function that represents the task of mapping a desired isobath. As is common in applications where plans must be continually updated based on real-time sensor measurements, we adopt a receding horizon framework where the vehicle continually computes near-optimal paths. The sequence of paths does not, in general, inherit the optimality properties of each individual path. Our real-time planning implementation incorporates recent results that lead to performance guarantees for receding-horizon planning.

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Section: Receding Horizon Path Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%