A multi‐objective approach for local path planning of autonomous mobile robot based on metaheuristics

Pattnaik, Sanat Kumar; Panda, S.; Mishra, Debadutta

doi:10.1002/cpe.6801

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Li et al [32] applied the improved artificial fish swarm algorithm to the path planning of mobile robots and smoothed the path by using the continuous segmented Bessel curve. Pattnaik et al [33] applied a chemical reaction optimization algorithm to the global path planning problem.…”

Section: Introductionmentioning

confidence: 99%

APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

et al. 2023

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Aiming at the problems of the A * algorithm in mobile robot path planning, such as multiple nodes, low path accuracy, long running time and difficult path initialization of particle swarm optimization, an APSO algorithm combining A * and PSO was proposed to calculate the optimal path. First, a redundant point removal strategy is adopted to preliminarily optimize the path planned by the A * algorithm and obtain the set of key nodes. Second, a stochastic inertia weight is proposed to improve the search ability of PSO. Third, a stochastic opposition-based learning strategy is proposed to further improve the search ability of PSO. Fourth, the global path is obtained by using the improved PSO to optimize the set of key nodes. Fifth, a motion time objective function that is more in line with the actual motion requirements of the mobile robot is used to evaluate the algorithm. The simulation results of path planning show that the path planned by APSO not only reduces the running time of the mobile robot by 17.35%, 14.84%, 15.31%, 15.21%, 18.97%, 15.70% compared with the A * algorithm in the six environment maps but also outperforms other path planning algorithms to varying degrees. Therefore, the proposed APSO is more in line with the actual movement of the mobile robot.INDEX TERMS A * algorithm, mobile robot, path planning, particle swarm optimization, stochastic inertia weight, stochastic opposition-based learning.

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Section: Introductionmentioning

confidence: 99%

APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

et al. 2023

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“…Global path planning focuses on planning on complete, known-environment maps to find the optimal route from the start point to the end point. Local path planning [15] is focused on rapid decision-making in local environments to avoid sudden obstacles or adapt to dynamic changes in the environment. This paper takes global path planning as the research object, realizes map partitioning through key points, and reduces the search scope of global path planning to achieve the purpose of greatly reducing the calculation amount of the global path planning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on preprocessing algorithm of indoor map partitioning and global path planning based on FAST

Yang,

Pan,

Liu

et al. 2023

Preprint

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Path planning is a critical factor in the successful performance of navigation tasks. This paper proposes a novel approach for indoor map partitioning and global path-planning preprocessing. The proposed algorithm aims to enhance the efficiency of path planning tasks by eliminating irrelevant areas. In view of the deformation problem encountered in the original indoor map partitioning method, initially, the contour detection algorithm is employed to identify and eliminate obstacles. Subsequently, the FAST algorithm is utilized to detect key points. These key points are then subjected to filtering and clustering using the K-means algorithm. Based on the 8-neighborhood characteristics, door points and inflection points within the room are selected. A clustering algorithm is employed to retain the door points, which are subsequently connected to form door line segments through averaging and filtering procedures. This process ensures the closure of the sub-room. Finally, the connected domain function is employed to extract the sub-room map, thereby completing the map partitioning process. Based on the sub-room map centroid coordinate point data obtained from the partitioning, two combinations are used as the starting point and the end point, respectively, and the A* algorithm is employed to calculate and store all path information from the starting point to the end point. Based on the sub-room map obtained through partitioning and the stored path information, the path is traversed to eliminate irrelevant areas, thereby achieving the preprocessing of global path planning. The simulation results showed that the A*, Bi-A*, JPS, Dijkstra, PRM, and RRT algorithms increased their rates by 18.2%, 43.6%, 20.5%, 31.9%, 29.1%, and 29.7%, respectively.

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Safe path planning of mobile robot based on improved particle swarm optimization

Guo,

Sun,

Chen

2024

Transactions of the Institute of Measurement and Control

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Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

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A multi‐objective approach for local path planning of autonomous mobile robot based on metaheuristics

Cited by 4 publications

References 35 publications

APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

Research on preprocessing algorithm of indoor map partitioning and global path planning based on FAST

Safe path planning of mobile robot based on improved particle swarm optimization

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