Robot Dynamic Path Planning Based on Improved A* and DWA Algorithms

Guan, Chenxi; Wang, Shuying

doi:10.1109/iccr55715.2022.10053929

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…It primarily performs discrete sampling within the allowed velocity space based on the current motion state of the drone and simulates the motion trajectories of these velocity combinations within the forward prediction time. Subsequently, the trajectory scores are determined by the evaluation function, leading to the identification of the optimal trajectory [28]. Subject to the constraints of motor performance and the environment, the velocity of the unmanned aerial vehicle at time t+1, denoted as,must satisfy the following constraints:…”

Section: Dynamic Window Approachmentioning

confidence: 99%

A new method for unmanned aerial vehicle path planning in complex environments

He,

Hou,

Wang

2024

Preprint

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In response to challenges in unmanned aerial vehicle (UAV) path planning, such as low search efficiency, non-smooth paths, and the inability to adapt to unknown environments, this paper proposes a novel UAV path planning method integrating a dual-layer optimization of A* and the dynamic window approach(DWA). Firstly, neighbor node pruning rules are defined to optimize the node expansion process of the A* algorithm. The heuristic function of the A* algorithm is dynamically adjusted based on obstacle coverage, and key path nodes are extracted using the Bresenham algorithm. Secondly, a deviation sub-function is introduced to enhance local path planning coherence, and an adaptive weighting method for the evaluation function is designed. Lastly, the optimized key nodes are utilized as sub-goals for local path planning, integrating the dynamic window approach to enhance real-time obstacle avoidance and adaptability in unknown environments. Simulation results demonstrate that the proposed method significantly improves the efficiency, smoothness, and obstacle avoidance performance of mobile robot path planning, meeting the requirements of complex planning tasks more effectively.

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Section: Dynamic Window Approachmentioning

confidence: 99%

A new method for unmanned aerial vehicle path planning in complex environments

He,

Hou,

Wang

2024

Preprint

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“…Additionally, an effective improvement direction involves merging the DWA algorithm with other algorithms, such as the A* algorithm first proposed by Hart in 1968 [21]. Extracting the key points of path planning by the improved A* algorithm as temporary target points of DWA algorithm, achieving the fusion of the improved A* and DWA algorithm on the basis of global optimization, thus realizing dynamic path planning in complex environments has been realized by reference [22]. In reference [23], the traditional A* algorithm's drawbacks, such as non-optimal path and many redundant points, were addressed by expanding its search neighborhood to remove the limitation of motion direction and combining it with the DWA's improved speed evaluation function for temporary obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

Wang

Cheng

Zhang

et al. 2023

Drones

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In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.

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“…The K Dan team proposed the A* algorithm for the first time [8]. Yang Guihua and others adopted the method of cleaning the Close list to optimize the total quantity of vertices in the route that the A* method designed [9], while Yang Mingliang and others tried to integrate the A* algorithm and The DWA algorithm into achieves the global optimality of the planned route via avoiding obstacles the objective is to arrive [10]. Although the above method has improved the efficiency and quality of path planning to a certain extent, it has not completely solved the problems of too many vertices in the path planning, is not smooth, and is easy to fall into a deadlock, and has not considered the cost of algorithm operation and the difficulty of the actual implementation.…”

Section: Introductionmentioning

confidence: 99%

Towards Path Planning Algorithm Combining with A-Star Algorithm and Dynamic Window Approach Algorithm

Li¹,

Gong²,

Tahir³

et al. 2023

IJACSA

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In the Automated Guided Vehicle (AGV) warehouse automatic guided vehicle system, the path planning algorithm for intelligent logistics vehicles is a key factor to ensure the stable and efficient operation of the system. However, the existing planning algorithms have problems such as single designing a route and the inability to intelligently evade moving barriers. The academic community has proposed various solutions to these problems, although they have improved the efficiency and quality of path planning to some extent, they have not completely solved problems such as poor safety in planning, the high number of path inflection points, poor path smoothness, easily getting stuck in deadlocks, and have not fully considered the running cost and practical implementation difficulty of algorithms. To address these issues, the article deeply researched traditional A* scheme and Dynamic Window Approach (DWA) technology and proposed designing a route method according to the fusion of the A* algorithm and DWA technology. The algorithm improved the A algorithm by introducing a sub-node optimization algorithm to solve problems for instance poor global path planning safety and easy deadlock. Moreover, the algorithm reduced the amount of global route reversal locations and increased path consistency by improving the evaluation function and removing redundant points of the A algorithm. Finally, by integrating the DWA algorithm, the intelligent logistics vehicle achieved dynamic obstacle avoidance capabilities for moving objects in the real world. Our simulations-based results on MATLAB framework show that the algorithm significantly improves path smoothness, path length, path planning time, and environmental adaptability compared to traditional algorithms, and basically meets the path planning requirements of the AGV system for intelligent logistics vehicles.

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Robot Dynamic Path Planning Based on Improved A* and DWA Algorithms

Cited by 9 publications

References 11 publications

A new method for unmanned aerial vehicle path planning in complex environments

A new method for unmanned aerial vehicle path planning in complex environments

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

Towards Path Planning Algorithm Combining with A-Star Algorithm and Dynamic Window Approach Algorithm

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