Adaptive Informed RRT*: Asymptotically Optimal Path Planning With Elliptical Sampling Pools in Narrow Passages

Huang, Yuan; Lee, Hee-Hyol

doi:10.1007/s12555-022-0834-9

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Planning is often applied to determine how to move along; the solution should be provided in a way that complies with the mechanical limitations of autonomous systems, such as robotics [1,2], graphic animation [3], manufacturing [4], and minimally invasive surgical procedures [5]. In the state space, the problem of robot motion planning [6][7][8] involves starting in some initial state and trying to arrive at a specified goal instead of goal states to minimize resource consumption, such as energy or time.…”

Section: Introductionmentioning

confidence: 99%

Double-Layer RRT* Objective Bias Anytime Motion Planning Algorithm

Esmaiel,

Zhao,

Qasem

et al. 2024

Robotics

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This paper proposes a double-layer structure RRT* algorithm based on objective bias called DOB-RRT*. The algorithm adopts an initial path with an online optimization structure for motion planning. The first layer of RRT* introduces a feedback-based objective bias strategy with segment forward pruning processing to quickly obtain a smooth initial path. The second layer of RRT* uses the heuristics of the initial tree structure to optimize the path by using reverse maintenance strategies. Compared with conventional RRT and RRT* algorithms, the proposed algorithm can obtain the initial path with high quality, and it can quickly converge to the progressive optimal path during the optimization process. The performance of the proposed algorithm is effectively evaluated and tested in real experiments on an actual wheeled robotic vehicle running ROS Kinetic in a real environment.

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