An Efficient Robot Exploration Method Based on Heuristics Biased Sampling

Liu, Jie; Lv, Yong; Yuan, Yuan; Chi, Wenzheng; Guodong, Cheng; Sun, Lining

doi:10.1109/tie.2022.3203762

Cited by 10 publications

(1 citation statement)

References 30 publications

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“…It can be seen from Equation (1) that every node expansion generation process is accompanied by a large number of samples. Therefore, to improve the state sampling points, this paper proposes a greedy target bias sampling method [ 22 ], which reduces the blindness of selecting

, as shown in Figure 3 . The algorithm generates new nodes in each sampling process and detects the distance

between the new node and the nearest obstacle in the direction of the target point in each iteration.…”

Section: Rrt Algorithm Based On Adaptive Step Size Strategymentioning

confidence: 99%

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment

Wang,

Yang,

Chen

et al. 2023

Biomimetics

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When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances.

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, as shown in Figure 3 . The algorithm generates new nodes in each sampling process and detects the distance

between the new node and the nearest obstacle in the direction of the target point in each iteration.…”

Section: Rrt Algorithm Based On Adaptive Step Size Strategymentioning

confidence: 99%

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment

Wang,

Yang,

Chen

et al. 2023

Biomimetics

View full text Add to dashboard Cite

show abstract

Motion control of obstacle avoidance for the robot arm via improved path planning algorithm

Fang,

Lu,

Zhang

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Autonomous robotic exploration with region-biased sampling and consistent decision making

Wang

Zheng

et al. 2023

Complex Intell. Syst.

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In this paper, we propose a scheme for autonomous exploration in unknown environments using a mobile robot. To reduce the storage consumption and speed up the search of frontiers, we propose a wave-features-based rapidly exploring random tree method, which can inhibit or promote the growth of sampling trees regionally. Then, we prune the frontiers with mean shift algorithm and use the pruned frontiers for decision-making. To avoid the repeated exploration, we develop a decision making method with consistency assessment, in which the status of the robot and frontiers are explicitly encoded and modeled as a fixed start open traveling salesman problem (FSOTSP). Furthermore, a re-decision mechanism is build to reduce the extra computing cost. Simulations and real-world experiments show the significant improvement of the proposed scheme.

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An Efficient Robot Exploration Method Based on Heuristics Biased Sampling

Cited by 10 publications

References 30 publications

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment

Motion control of obstacle avoidance for the robot arm via improved path planning algorithm

Autonomous robotic exploration with region-biased sampling and consistent decision making

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