Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search
نویسندگان
چکیده
منابع مشابه
Informed Asymptotically Optimal Anytime Search
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 respectively used by informed graph-based searches and anytime sampling-based planners. Informed graph-bas...
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Anytime almost-surely asymptotically optimal planners, such as RRT*, incrementally find paths to every state in the search domain. This is inefficient once an initial solution is found as then only states that can provide a better solution need to be considered. Exact knowledge of these states requires solving the problem but can be approximated with heuristics. This paper formally defines thes...
متن کاملBIT*: Batch Informed Trees for Optimal Sampling-based Planning via Dynamic Programming on Implicit Random Geometric Graphs
Path planning in continuous spaces has traditionally been divided between discrete and sampling-based techniques. Discrete techniques use the principles of dynamic programming to solve a discretized approximation of the problem, while sampling-based techniques use random samples to perform a stochastic search on the continuous state space. In this paper, we use the fact that stochastic planners...
متن کاملInformed Asymptotically Near-Optimal Planning for Field Robots with Dynamics
Recent progress in sampling-based planning has provided performance guarantees in terms of optimizing trajectory cost even in the presence of significant dynamics. The STABLE SPARSE RRT (SST) algorithm has these desirable path quality properties and achieves computational efficiency by maintaining a sparse set of state-space samples. The current paper focuses on field robotics, where workspace ...
متن کاملSearching Informed Game Trees
Well-known algorithms for the evaluation of the minimax function in game trees are alpha-beta Knuth] and SSS* Stockman]. An improved version of SSS* is SSS-2 Pijls-1]. All these algorithms don't use any heuristic information on the game tree. In this paper the use of heuristic information is introduced into the alpha-beta and the SSS-2 algorithm. Extended versions of these algorithms are presen...
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ژورنال
عنوان ژورنال: The International Journal of Robotics Research
سال: 2020
ISSN: 0278-3649,1741-3176
DOI: 10.1177/0278364919890396