Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

Yao, Qingfeng; Zheng, Zeyu; Qi, Liang; Yuan, Haitao; Guo, Xiwang; Zhao, Ming; Liu, Zhi; Yang, Tianji

doi:10.1109/access.2020.3011211

Cited by 137 publications

(64 citation statements)

References 38 publications

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“…e inertia weight update formula of the traditional PSO algorithm is shown as (12). T is the number of current iterations, and the inertia weight ω (i) decreases linearly with the increase of T. Compared with the inertia weight formula of the traditional PSO, the inertia weight for RMPSO algorithm can be adjusted adaptively according to the surrounding environment, so the convergence rate of RMPSO increases.…”

Section: Pso Weight Updating Functionmentioning

confidence: 99%

“…e AUV path planning algorithm can be divided into local path planning and global path planning according to the environment information. Local path planning, like rolling window algorithm [10] and artificial potential field method [11,12], aims to avoid the obstacles quickly when a robot's sensor detects the surrounding obstacles. Global path planning, such as A * algorithm [13,14], fast search algorithm [15], Dijkstra algorithm [16], probabilistic roadmaps [17], estimation of distribution algorithm (EDA) based approach [18], is a kind of path planning method used when the map environment is known.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Huang

Jin

2021

Complexity

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In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.

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Section: Pso Weight Updating Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Huang

Jin

2021

Complexity

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“…For this reason, the APF algorithm is intensively studied to increase the quality of the movement and reduce the stagnation in local minima. In [10], the APF algorithm was combined with improved black-hole potential field and reinforcement learning to solve the problems, which are scenarios of local minima. The feature of the algorithm is that mobile robots can jump out of local minima without prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

2021

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Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The Artificial Potential Field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of Artificial Potential Field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel Artificial Potential Field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

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“…The current path planning algorithms mainly include the colony algorithms (Liu et al, 2019;Ye et al, 2020;Zhang et al, 2020;Zhu et al, 2020), PSO (Krell et al, 2019;Wang Y. B. et al, 2019;Liu X. H. et al, 2021;Song et al, 2021), A * algorithms (Xiong et al, 2020;Tang et al, 2021;Tullu et al, 2021), artificial potential field methods Azmi and Ito, 2020;Song et al, 2020;Yao et al, 2020), genetic algorithms (Hao et al, 2020;Li K. R. et al, 2021;Wen et al, 2021), fuzzy control algorithms (Guo et al, 2020;Zhi and Jiang, 2020), fast marching algorithms (Sun et al, 2021;Wang et al, 2021;Xu et al, 2021), and deep reinforcement learning algorithms (Li L. Y. et al, 2021;Lin et al, 2021;Xie et al, 2021). PSO is an evolutionary computation algorithm that can be used to find the optimal solution through collaboration and information sharing between individuals in the group, as in path planning, the optimal solution is to find the shortest path.…”

Section: Introductionmentioning

confidence: 99%

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Cheng

Zheng³

et al. 2021

Front. Neurorobot.

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The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

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Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

Cited by 137 publications

References 38 publications

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

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