Robot obstacle avoidance optimization by A* and DWA fusion algorithm

Li, Peiying; Hao, Lingjuan; Zhao, Yanjie; Lu, Jianmin

doi:10.1371/journal.pone.0302026

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…The four algorithms for the same environment took the (40,13) coordinates as the starting point and the (28,14) coordinates as the ending point. The APF-A * optimized by ( 16) at (22,23) reduces three redundant inflection points compared with the conventional A * algorithm. The comparison yields the data shown in table 2.…”

Section: Algorithms Comparison Experimentsmentioning

confidence: 99%

“…However, all of them only consider the problems of local minimum and target unreachability and have yet to fully consider the problems of removing redundant nodes and extra inflection points of trajectories. Li et al [22] proposed a robot trajectory planning method based on improved A * and dynamic window approach (DWA) fusion algorithms (DWA-A * ). Based on this, the obstacle avoidance algorithm is implemented.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory planning for AGV based on the improved artificial potential field- A* algorithm

Liu,

Chen,

Wang

et al. 2024

Meas. Sci. Technol.

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There are many redundant nodes and inflection points in the path planned by the traditional A* algorithm, leading to the inefficient trajectory planning of the automatic guided vehicle (AGV) in the multi-static obstacles environment. The artificial potential field (APF) algorithm suffers from the problem of unreachable objectives and falling into optimal local value. This article studies the trajectory optimization of AGVs to improve the trajectory planning algorithm’s iteration efficiency and shorten the trajectory’s total length. This article establishes the forward kinematic and unified robot description format model of the AGV and proposes the APF-A* algorithm for trajectory planning. The search cost and the number of turns are effectively optimized. The article simulates the APF-A* algorithm, the results are compared with the trajectory before optimization, and the optimized time is 60% less than that before optimization. The experimental platform of AGV trajectory planning is built, and the algorithm verification experiment of AGV trajectory planning is carried out. The experimental results show that the algorithm studied in this article achieves path smoothing and trajectory length optimization.

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Section: Algorithms Comparison Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory planning for AGV based on the improved artificial potential field- A* algorithm

Liu,

Chen,

Wang

et al. 2024

Meas. Sci. Technol.

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An improved fuzzy‐controlled local path planning algorithm based on dynamic window approach

Liu,

et al. 2024

Journal of Field Robotics

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With the increasingly complex operating environment of mobile robots, the intelligent requirements of robots are getting higher and higher. Navigation technology is the core of mobile robot intelligent technology research, and path planning is an important function of mobile robot navigation. Dynamic window approach (DWA) is one of the most popular local path planning algorithms nowadays. However, there are also some problems. DWA algorithm is easy to fall into local optimal solution without the guidance of global path. The traditional solution is to use the key nodes of the global path as the temporary target points. However, the guiding ability of the temporary target points will be weakened in some cases, which still leads DWA to fall into local optimal solutions such as being trapped by a “C”‐shaped obstacle or go around outside of a dense obstacle area. In a complex operating environment, if the local path deviates too far from the global path, serious consequences may be caused. Therefore, we proposed a trajectory similarity evaluation function based on dynamic time warping method to provide better guidance. The other problem is poor adaptability to complex environments due to fixed evaluation function weights. And, we designed a fuzzy controller to improve the adaptability of the DWA algorithm in complex environments. Experiment results show that the trajectory similarity evaluation function reduces algorithm execution time by 0.7% and mileage by 2.1%, the fuzzy controller reduces algorithm execution time by 10.8% and improves the average distance between the mobile robot and obstacles at the global path's danger points by 50%, and in simulated complex terrain environment, the finishing rate of experiments improves by 25%.

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Path planning strategies for logistics robots: Integrating enhanced A‐star algorithm and DWA

Zeng,

Zhang,

Yin

et al. 2024

Electronics Letters

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Path planning is the key part in the process of transportation conducted by logistics robots, and there often exist some problems with it. The path designed is not always smooth enough and its search efficiency is low, for example. As a common global path planning algorithm, A‐star is based on the traditional algorithm, which is unable to solve the problem of uneven path in the movement of logistics robots. Through improving the heuristic function of the traditional A‐star algorithm, weighing the heuristic function dynamically, removing the redundant points of the traditional star algorithm path with Floyd algorithm, and setting a safe distance to prevent the logistics robot from collision at the same time, the path is finally curved to be more appropriate to the movement path of the logistics robot. The MATLAB simulation of A‐star algorithm before and after the improvement shows that the turning points of the advanced A‐star algorithm reduced 61.5% on average compared to the traditional algorithm. The path length decreased 2.4% and the traversing points reduced 58.5%. At the same time, the DWA algorithm introduces dynamic weight coefficients, which can dynamically adjust the weight coefficients when encountering obstacles, so as to safely reach the target point.

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Robot obstacle avoidance optimization by A* and DWA fusion algorithm

Cited by 5 publications

References 22 publications

Trajectory planning for AGV based on the improved artificial potential field- A* algorithm

Trajectory planning for AGV based on the improved artificial potential field- A* algorithm

An improved fuzzy‐controlled local path planning algorithm based on dynamic window approach

Path planning strategies for logistics robots: Integrating enhanced A‐star algorithm and DWA

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