Asymptotically optimal sampling-based kinodynamic planning
نویسندگان
چکیده
منابع مشابه
Asymptotically optimal sampling-based kinodynamic planning
Sampling-based algorithms are viewed as practical solutions for high-dimensional motion planning. Recent progress has taken advantage of random geometric graph theory to show how asymptotic optimality can also be achieved with these methods. Achieving this desirable property for systems with dynamics requires solving a two-point boundary value problem (BVP) in the state space of the underlying ...
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Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion planning problem lies in a Euclidean s...
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Self-reconfigurable robots are composed of many individual modules that can autonomously move to transform the shape and structure of the robot. In this paper we present a kinodynamically optimal algorithm for the following “x-axis to yaxis” reconfiguration problem: given a horizontal row of n modules, reconfigure that collection into a vertical column of n modules. The goal is to determine the...
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Abstract. This work describes STABLE SPARSE RRT (SST), an algorithm that (a) provably provides asymptotic (near-)optimality for kinodynamic planning without access to a steering function, (b) maintains only a sparse set of samples, (c) converges fast to high-quality paths and (d) achieves competitive running time to RRT, which provides only probabilistic completeness. SST addresses the limitati...
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ژورنال
عنوان ژورنال: The International Journal of Robotics Research
سال: 2016
ISSN: 0278-3649,1741-3176
DOI: 10.1177/0278364915614386