Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Wang, Pengyu; Liu, Yanglin; Yao, Weimin; Yu, Yuanbin

doi:10.1177/09544070221100677

Cited by 17 publications

(10 citation statements)

References 44 publications

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“…This method effectively handles the multi-objective nature of the problem and has universality and adaptability for path planning on a practical scale. Among them, the A-star algorithm, as a classic path planning algorithm, performs well in calculating the global optimal path and plays a key role in robot navigation, game NPC pathfinding, and other fields [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning with the Integration of Adaptive A-Star Algorithm and Improved Dynamic Window Approach

Liao,

Chen,

et al. 2024

Electronics

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In response to the shortcomings of the traditional A-star algorithm, such as excessive node traversal, long search time, unsmooth path, close proximity to obstacles, and applicability only to static maps, a path planning method that integrates an adaptive A-star algorithm and an improved Dynamic Window Approach (DWA) is proposed. Firstly, an adaptive weight value is added to the heuristic function of the A-star algorithm, and the Douglas–Pucker thinning algorithm is introduced to eliminate redundant points. Secondly, a trajectory point estimation function is added to the evaluation function of the DWA algorithm, and the path is optimized for smoothness based on the B-spline curve method. Finally, the adaptive A-star algorithm and the improved DWA algorithm are integrated into the fusion algorithm of this article. The feasibility and effectiveness of the fusion algorithm are verified through obstacle avoidance experiments in both simulation and real environments.

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Section: Introductionmentioning

confidence: 99%

Research on Path Planning with the Integration of Adaptive A-Star Algorithm and Improved Dynamic Window Approach

Liao,

Chen,

et al. 2024

Electronics

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“…In a study by Hong et al [39] aiming at the long planning time of long-distance cross-country paths, the planning efficiency was improved by integrating data structures and improving retrieval strategies. Wang et al [40] proposed an improved A* algorithm based on the heuristic function of collision cost based on the position of obstacles. Considering vehicle profile and speed, safe space is set around the vehicle to improve vehicle safety.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

Wang

Zhao

et al. 2023

Electronics

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This study is concerned with path planning in a structured greenhouse, in contrast to much of the previous research addressing applications in outdoor fields. The prototype mainly comprises an independently driven Mecanum wheel, a lidar measuring module, a single-chip microcomputer control board, and a laptop computer. Environmental information collection and mapping were completed on the basis of lidar and laptop computer connection. The path planning algorithm used in this paper expanded the 8-search-neighborhood of the traditional A* algorithm to a 48-search-neighborhood, increasing the search direction and improving the efficiency of path planning. The Floyd algorithm was integrated to smooth the planned path and reduced the turning points in the path. In this way, the problems of the traditional A* algorithm could be solved (i.e., slow the path planning speed and high numbers of redundant points). Tests showed that the turning points, planning path time, and distance of the improved algorithm were the lowest. Compared with the traditional 8-search-neighborhood A* algorithm, the turning point was reduced by 50%, the planning time was reduced by 13.53%, and the planning distance was reduced by 13.96%. Therefore, the improved method of the A* algorithm proposed in this paper improves the precision of the planning path and reduces the planning time, providing a theoretical basis for the navigation, inspection, and standardization construction of greenhouses in the future.

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“…Global path planning is carried out throughout the entire map range, planning a rough route for vehicles from the starting point to the endpoint. The classification of common global path planning methods is as follows, graph search-based algorithms such as A* [ 15 ] and Dijkstra [ 16 ], intelligent algorithms such as the genetic algorithm [ 17 ] and particle swarm optimization [ 18 ], and machine learning-based methods such as reinforcement learning [ 19 ] and deep learning [ 20 ]. Local path planning is carried out in the environment surrounding the vehicle’s current location.…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC

Zhang,

Liu,

Gao

et al. 2023

Sensors

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Path planning and tracking control is an essential part of autonomous vehicle research. In terms of path planning, the artificial potential field (APF) algorithm has attracted much attention due to its completeness. However, it has many limitations, such as local minima, unreachable targets, and inadequate safety. This study proposes an improved APF algorithm that addresses these issues. Firstly, a repulsion field action area is designed to consider the velocity of the nearest obstacle. Secondly, a road repulsion field is introduced to ensure the safety of the vehicle while driving. Thirdly, the distance factor between the target point and the virtual sub-target point is established to facilitate smooth driving and parking. Fourthly, a velocity repulsion field is created to avoid collisions. Finally, these repulsive fields are merged to derive a new formula, which facilitates the planning of a route that aligns with the structured road. After path planning, a cubic B-spline path optimization method is proposed to optimize the path obtained using the improved APF algorithm. In terms of path tracking, an improved sliding mode controller is designed. This controller integrates lateral and heading errors, improves the sliding mode function, and enhances the accuracy of path tracking. The MATLAB platform is used to verify the effectiveness of the improved APF algorithm. The results demonstrate that it effectively plans a path that considers car kinematics, resulting in smaller and more continuous heading angles and curvatures compared with general APF planning. In a tracking control experiment conducted on the Carsim–Simulink platform, the lateral error of the vehicle is controlled within 0.06 m at both high and low speeds, and the yaw angle error is controlled within 0.3 rad. These results validate the traceability of the improved APF method proposed in this study and the high tracking accuracy of the controller.

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Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Cited by 17 publications

References 44 publications

Research on Path Planning with the Integration of Adaptive A-Star Algorithm and Improved Dynamic Window Approach

Research on Path Planning with the Integration of Adaptive A-Star Algorithm and Improved Dynamic Window Approach

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC

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