Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

Zheng, Lei; Wang, Yu; Li, Guangxu; Qin, G.; Luo, Yunchuan

doi:10.3390/s23136082

Cited by 18 publications

(13 citation statements)

References 37 publications

(42 reference statements)

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“…To address the obstacle avoidance for the walking robot, the APF method is employed. The APF method allows an object to navigate within gravitational and repulsive fields and reach a target point [31][32][33][34][35]. The gravitational potential field function (PFF) U att (X) generated by the target point to robot's current position can be expressed as:…”

Section: Classic Apf Principles Methodsmentioning

confidence: 99%

Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot

Tang,

Pei,

Zhang

2024

Sensors

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Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues.

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Section: Classic Apf Principles Methodsmentioning

confidence: 99%

Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot

Tang,

Pei,

Zhang

2024

Sensors

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“…The algorithm flow is shown in Algorithm 1. To ensure that the flight safety of multi-UAVs in the mission avoids internal collision [40], we designed a multi-UAV drone avoidance control algorithm based on the traditional artificial potential field method. The algorithm extends the multilayer potential field function to improve the UAV avoidance performance based on the traditional artificial potential field algorithm with a gravitational potential field of…”

Section: Search Conversion Strategymentioning

confidence: 99%

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Liang,

Li,

2023

Sensors

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To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.

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“…This enhancement aims to provide a more comprehensive navigation strategy. The resulting optimized evaluation function is mathematically depicted by Equation (10).…”

Section: Optimization Of the Evaluation Functionmentioning

confidence: 99%

“…Currently, numerous AGV path-planning algorithms cater to diverse environments. Common static global path planning algorithms encompass the A* algorithm [3,4], Dijkstra's algorithm [5,6], ant colony algorithm [7,8], genetic algorithm [9], and particle swarm optimization algorithm [10,11]. Meanwhile, local path planning algorithms comprise the dynamic window approach (DWA) [12,13], the artificial potential field method [14,15], and the time elastic band (TEB) algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

Guo,

Sun,

Liu

et al. 2023

Applied Sciences

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Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm’s evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm’s evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms.

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Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

Cited by 18 publications

References 37 publications

Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot

Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

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