Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method

Zhang, Su Ying; Shen, Yan; Cui, Wen

doi:10.4028/www.scientific.net/amm.644-650.154

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…Based on the concept of potential field in physics [31] , the artificial potential field algorithm models the robot and the environment as a potential field system, and determines the moving direction of the robot by calculating and optimizing the potential field.…”

Section: Main Problems Of Artificial Potential Field Algorithmmentioning

confidence: 99%

Research on dynamic and static obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Hu,

Niu,

Gao

et al. 2024

Preprint

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Aiming at the problem that Automated guided vehicle (AGV) faces dynamic and static obstacles in the process of transporting patients in medical environment and needs to avoid in real time, inspired by the behavior of antelopes during migration, this paper proposes a bionic obstacle avoidance strategy based on adaptive behavior of antelopes. The artificial potential field and dynamic window algorithm are improved by using the decircling strategy and conical visual distance during antelope migration. The limitations of traditional artificial potential field algorithm are improved by adding new potential field force points into the global planning, and the real-time obstacle avoidance ability of medical AGV is enhanced. The evaluation index of the algorithm is improved in local path planning, and the prediction range of AGV navigation is enlarged. Simulation experiments were carried out through the numerical simulation platform, and the verification results showed that:The bionic obstacle avoidance strategy proposed in this paper can avoid dynamic and static obstacles at the same time. In the example, the success rate of path planning is increased by 34%, the running time is reduced by 33%, and the average path length is reduced by 1%. The proposed method can realize the integration of "dynamic and static" avoidance in the process of transporting patients, and effectively save the time of transporting patients by AGV.It provides a theoretical basis for realizing obstacle avoidance and rapid loading of AGV in medical environment.

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Section: Main Problems Of Artificial Potential Field Algorithmmentioning

confidence: 99%

Research on dynamic and static obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Hu,

Niu,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To address the issue that the target point is unreachable, it is necessary to optimize the repulsion function and add the distance factor d 2 g [34]. The improved repulsive potential field function is as follows:…”

Section: Increase Distance Factormentioning

confidence: 99%

Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method

Li,

Bian

2024

Sensors

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For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving.

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“…To solve the local minimum problem caused by APF, some researchers have done dome evolutionary work. A relative distance was proposed in the Literature [8] when defined new repulsive potential field function. The relative distance is the distance between the robot and the goal, thus it can ensure the goal is in the only minimum point.…”

Section: Theory Of Evolutionary Apfmentioning

confidence: 99%

An Optimization Path Planning Method Based on Evolutionary Artificial Potential Field

2013

AMM

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This paper presents an evolutionary way for the robot to plan path. The way is based on the Evolutionary Artificial Potential Field approach. APF is an efficient way for a robot to plan its path, and the evolutionary APF can help the robot to jump out of the local minimum point. A matrix is integrated in the new algorithm. The matrix can modify the direction of a robot when the robot is trapped in a local minimum point. The force which has been changed will prompt the robot to escape from the local minimum point. Simulation result shows that the optimized algorithm is an effective way to solve the local minimum problem.

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Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method

Cited by 12 publications

References 3 publications

Research on dynamic and static obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Research on dynamic and static obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method

An Optimization Path Planning Method Based on Evolutionary Artificial Potential Field

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