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

Yu, Guo; Wang, Yimiao; Zhao, Xiong; Chen, Yijin; Liu, Jiangang

doi:10.3390/electronics12081754

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…When global information is known, the A* algorithm can quickly achieve path planning through node state detection and simple estimation. However, this algorithm ignores the motion constraints on the robot, resulting in too many planned path turning points and curvature jumps, which is not in line with kinematic principles [17,18]. In response to the problem of too many turning points in the traditional A* algorithm, Lao et al [19] optimized the key point selection strategy.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Liu,

Wang,

Wang

et al. 2024

Electronics

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In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently, based on fuzzy control theory and sliding mode variable structure control theory, we established a kinematic model for tracked agricultural machinery as the control object, designed a fuzzy sliding mode approaching law, and preprocessed it to reduce the time required for sliding mode control to reach the chosen stage. The simulation experiment of path planning shows that compared with A*, the average reduction rate of the path length for IA* is 5.51%, and the average reduction rate of the number of turning points is 39.01%. The path tracking simulation experiment shows that when the driving speed is set to 0.2 m/s, the adjustment time of the F-SMC controller is reduced by 0.99 s and 1.42 s compared to the FUZZY controller and PID controller, respectively. The variance analysis of the adjustment angle shows that the minimum variance of the F-SMC controller is 0.086, and the error converges to 0, proving that the vehicle trajectory is smoother and ultimately achieves path tracking. The field test results indicate that the path generated by the IA* algorithm can be tracked by the F-SMC controller in the actual environment. Compared to the A* algorithm and FUZZY controller, the path tracking time reduction rate of IA* and F-SMC is 29.34%, and the fuel consumption rate is reduced by 2.75%. This study is aimed at providing a feasible approach for improving the efficiency of tracked agricultural machinery operations, reducing emissions and operating costs.

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Section: Introductionmentioning

confidence: 99%

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Liu,

Wang,

Wang

et al. 2024

Electronics

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“…Researchers and engineers are working diligently to refine the algorithm, thereby introducing modifications and supplementary techniques to ensure that it remains a valuable asset in the modernization and optimization of agricultural practices. Overcoming this obstacle will further solidify the A-star algorithm's position as a transformative force in the agricultural industry, thus facilitating precision farming and sustainable food production [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture

Chatzisavvas,

Dossis,

Dasygenis

2024

Electronics

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The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness. In response to this challenge, particularly in agricultural operations, this research endeavours to enhance the evaluation of individual nodes within the search procedure and improve the overall smoothness of the resultant path. So, to mitigate the inherent choppiness of A-star-generated paths in agriculture, this work adopts a novel approach. It introduces utilizing Bezier curves as a postprocessing step, thus refining the generated paths and imparting their smoothness. This smoothness is instrumental for real-world applications where continuous and safe motion is imperative. The outcomes of simulations conducted as part of this study affirm the efficiency of the proposed methodology. These results underscore the capability of the enhanced technique to construct smooth pathways. Furthermore, they demonstrate that the generated paths enhance the overall planning performance. However, they are also well suited for deployment in rural conditions, where navigating complex terrains with precision is a critical necessity.

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“…Although this method can make the path smoother, it still has redundant useless nodes. To solve this problem, Xu et al [14][15] optimized the global path points output by the improved A* algorithm using Floyd's algorithm, eliminating the redundant nodes and reducing the path length and number of turns. However, the heuristic function of this method was not optimized, resulting in inefficient pathfinding.…”

Section: Introductionmentioning

confidence: 99%

Research on fusing improved A* and adaptive DWA path planning algorithms

LIAO,

WANG,

QIN

2024

Preprint

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To satisfy the performance requirements of robot path planning, an algorithm combining the improved A* algorithm and the improved Dynamic Window Approach (DWA) is proposed, which results in shorter path lengths, improved search efficiency, and path smoothness. Aiming at the challenges of the traditional A* algorithm in complex scenarios, a new heuristic function based on Manhattan and diagonal is designed, and then weights are dynamically assigned to obtain the global shortest path and the least search time. Then, an improved search strategy based on 8-neighborhoods is proposed, which improves the search efficiency and reduces the time consumption of the traditional 8-neighborhood 8-direction search method by dynamically assigning the optimal search direction of the current node. On the other hand, the traditional DWA algorithm faces some challenges, such as the paths are not globally optimal, the path planning may fail or path length may increase, the azimuthal coefficient is rigid, and the algorithm is computationally intensive. For these problems, a keypoint densification strategy is proposed to modify the deflected paths, adaptively adjust the azimuth function coefficients, and limit the range of the obstacle distance function. Finally, the proposed improved A* algorithm and fusion algorithm are compared with the existing methods. The simulation results under the ROS system show that the improved A* algorithm can generate the shortest global path in complex environments, the average path length is reduced by 3.95%, and the average path searching time is shortened by 21.62%. For the fused algorithm, the average path length and the average runtime are reduced by 5.95% and 8.7% in the moving obstacles environment.

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

Cited by 7 publications

References 43 publications

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture

Research on fusing improved A* and adaptive DWA path planning algorithms

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