The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

Zhai, Shuangzhu; Pei, Yulong

doi:10.3390/su152115377

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…The solution to (32) gives the potential of a singular point of power q at (0,0) in a 2D Cartesian (x, y):…”

Section: Methods Based On Sliding Mode (Sm)mentioning

confidence: 99%

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Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

Katona,

Neamah,

Korondi

2024

Sensors

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Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra’s algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies.

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“…The solution to (32) gives the potential of a singular point of power q at (0,0) in a 2D Cartesian (x, y):…”

Section: Methods Based On Sliding Mode (Sm)mentioning

confidence: 99%

“…Furthermore, ref. [ 32 ] outlines an active obstacle avoidance method for autonomous vehicles that is also based on an improved APF.…”

Section: Classic Approachesmentioning

confidence: 99%

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

Katona,

Neamah,

Korondi

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Xu [ 14 ] engineered a spring mechanism in the repulsive velocity potential field to effectively eliminate the flutter phenomenon near the obstacle. Zhai [ 15 ] and others added an adjustment factor and a judgement coefficient to the potential field to optimize the potential field function and improve driving comfort and the safety of the planned path. Many other researchers have proposed fusion algorithms combining the advantages of the two algorithms.…”

Section: Introductionmentioning

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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The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

Cited by 2 publications

References 27 publications

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

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

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