A probability smoothing Bi-RRT path planning algorithm for indoor robot

Ma, Guojun; Duan, Yunlong; Li, Mingze; Xie, Zhibin; Zhu, Jin

doi:10.1016/j.future.2023.02.004

Cited by 24 publications

(13 citation statements)

References 30 publications

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“…However, heuristic algorithms, as a primary approach to path planning, exhibit significant limitations in addressing the diversity and complexity of practical application environments. These traditional heuristic algorithms struggle to meet the requirements of more complex path planning scenarios due to inherent limitations [7][8][9]. For instance, Shengmin Zhao et al [7] proposed an innovative full-coverage path planning (CCPP) method, effectively addressing the dynamic tracking problem and solving multi-obstacle challenges in complex environments.…”

Section: Introductionmentioning

confidence: 99%

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Meng,

Fang

2024

Electronics

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Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm’s challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm’s issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios.

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Section: Introductionmentioning

confidence: 99%

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Meng,

Fang

2024

Electronics

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“…The first three types of methods can be widely applied to mobile robots in two-dimensional planes, but they are difficult to be applied to path planning in the joint space for robots with high degrees of freedom. The basic RRT algorithm and its variants, such as RRT * [21] and Bi-RRT [22], are tree structure algorithms based on random sampling, which are widely used in motion planning for multidegree-of-freedom robots. However, the RRT algorithm has drawbacks such as strong randomness, long planning time, and poor path smoothness.…”

Section: Introductionmentioning

confidence: 99%

Path planning for robots in preform weaving based on learning from demonstration

Meng,

Li,

Zhang

et al. 2024

Robotica

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A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.

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“…Accordingly, the RRT algorithm, employing a globally uniform random sampling strategy for rapidly expanding new nodes, stands out as an optimal choice for scenarios requiring rapid path planning [ 24 , 25 , 26 , 27 ]. However, as task complexity increases, researchers have identified some limitations in RRT, such as low node utilization [ 28 , 29 ] and path instability [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Improved RRT* Algorithm for Disinfecting Robot Path Planning

Wang,

Zhou,

et al. 2024

Sensors

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In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm’s convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm’s enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

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A probability smoothing Bi-RRT path planning algorithm for indoor robot

Cited by 24 publications

References 30 publications

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Path planning for robots in preform weaving based on learning from demonstration

Improved RRT* Algorithm for Disinfecting Robot Path Planning

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