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Efficient Robot Motion Planning Using Bidirectional-Unidirectional RRT Extend Function
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AI
DOI：10.1109/TASE.2021.3130372.png)
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Abstract
En 中文
In this article, based on the rapidly-exploring random tree (RRT), we propose a novel and efficient motion planning algorithm using bidirectional RRT search. First, a RRT extend function is used to organize the sampled states under kinodynamic constraints. Meanwhile, the bidirectional search strategy is implemented to grow a forward tree and backward tree simultaneously in the tree extension process. When these two trees meet each other, the backward tree will act as a heuristic to guide the forward tree to continuously grow toward the goal state, where the algorithm switches to unidirectional search mode. Therefore, the two-point boundary value problem (BVP) in the connection process is avoided, and the extension process gets much accelerated. We also prove that probabilistic completeness is guaranteed. Numerical simulations are conducted to demonstrate that the proposed algorithm performs much better than the state-of-the-art algorithms in different environments.
Keywords:
Planning
Heuristic algorithms
Robot motion
Search problems
Robots
Trajectory
Robot kinematics
Robotics
robot motion planning
rapidly-exploring random tree
bidirectional search
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