Multi-Heuristic A*

Aine, Sandip; Swaminathan, Siddharth; Narayanan, Venkatraman; Hwang, Victor; Likhachev, Maxim

doi:10.15607/rss.2014.x.056

Cited by 25 publications

(17 citation statements)

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“…There is also a clear opportunity to consider different anytime approximations, such as deterministic sampling (Janson et al, 2018) or adaptive meshes (Yershov and Frazzoli, 2016), and more advanced graph-based-search techniques, such as Anytime Repairing A * (ARA * ; Likhachev et al, 2008) and Multi-Heuristic A * (MHA * ; Aine et al, 2015), to further accelerate the search performance of BIT * .…”

Section: Discussionmentioning

confidence: 99%

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Gammell

Barfoot

Srinivasa

2020

The International Journal of Robotics Research

108

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Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively. Informed graph-based searches, such as A*, traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient, but leaves their performance dependent on the chosen approximation. If the resolution of the chosen approximation is too low, then they may not find a (suitable) solution, but if it is too high, then they may take a prohibitively long time to do so. Anytime sampling-based planners, such as RRT*, traditionally use random sampling to approximate the problem domain incrementally. This allows them to increase resolution until a suitable solution is found, but makes their search dependent on the order of approximation. Arbitrary sequences of random samples approximate the problem domain in every direction simultaneously, but may be prohibitively inefficient at containing a solution. This article unifies and extends these two approaches to develop Batch Informed Trees (BIT*), an informed, anytime sampling-based planner. BIT* solves continuous path planning problems efficiently by using sampling and heuristics to alternately approximate and search the problem domain. Its search is ordered by potential solution quality, as in A*, and its approximation improves indefinitely with additional computational time, as in RRT*. It is shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.

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Section: Discussionmentioning

confidence: 99%

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Gammell

Barfoot

Srinivasa

2020

The International Journal of Robotics Research

108

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“…An immediate question is how to efficiently combine the benefits of different metrics. This resembles the idea of combining different heuristics in search-based algorithms (Aine et al, 2016). We propose to borrow ideas from this domain to address our problem.…”

Section: Discussionmentioning

confidence: 99%

“…One approach may be to grow several trees, one for each metric. The trees can share states (such as SMHA* (Aine et al, 2016)) and choosing which tree to grow at each point can be done in a dynamic fashion (see, e.g., Phillips et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Effective metrics for multi-robot motion-planning

Atias

Solovey

Salzman³

et al. 2018

The International Journal of Robotics Research

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We study the effectiveness of metrics for Multi-Robot Motion-Planning (MRMP) when using RRT-style sampling-based planners. These metrics play the crucial role of determining the nearest neighbors of configurations and in that they regulate the connectivity of the underlying roadmaps produced by the planners and other properties like the quality of solution paths. After screening over a dozen different metrics we focus on the five most promising ones-two more traditional metrics, and three novel ones which we propose here, adapted from the domain of shape-matching. In addition to the novel multi-robot metrics, a central contribution of this work are tools to analyze and predict the effectiveness of metrics in the MRMP context. We identify a suite of possible substructures in the configuration space, for which it is fairly easy (i) to define a so-called natural distance, which allows us to predict the performance of a metric. This is done by comparing the distribution of its values for sampled pairs of configurations to the distribution induced by the natural distance; (ii) to define equivalence classes of configurations and test how well a metric covers the different classes. We provide experiments that attest to the ability of our tools to predict the effectiveness of metrics: those metrics that qualify in the analysis yield higher success rate of the planner with fewer vertices in the roadmap. We also show how combining several metrics together leads to better results (success rate and size of roadmap) than using a single metric.

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“…escape local minima of a potential function (4) or design a potential function that has only one minimum (5). Another family of planning algorithms is composed of heuristic search techniques [e.g., A* (6)] that operate over a discretization of possible robot configurations. These algorithms provide resolution completeness: A path will be found if the discretization is fine enough (7,8).…”

Section: Figurementioning

confidence: 99%

Sampling-Based Methods for Motion Planning with Constraints

Kingston

Moll

Kavraki

2018

Annu. Rev. Control Robot. Auton. Syst.

148

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Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: (a) sampling constraintsatisfying configurations and (b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints. 3.1

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Multi-Heuristic A*

Cited by 25 publications

References 0 publications

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Effective metrics for multi-robot motion-planning

Sampling-Based Methods for Motion Planning with Constraints

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