GPU-based Global Path Planning Using Genetic Algorithm with Near Corner Initialization

Ou, Junlin; Hong, Sampyo; Ziehl, Paul; Wang, Yi

doi:10.1007/s10846-022-01576-6

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…,15,1), (5,22,5), (15,11,2), (0,15,2), (5,22,2), (11,24,3), (5,20 From the simulation results of two distinct complex environments, it is evident that the proposed improved A * algorithm can effectively perform obstacle avoidance planning and that the planned path in a complex environment with numerous obstacles is relatively short. Consequently, the enhanced A * algorithm has enhanced adaptability to complex environments and a degree of generality in path planning.…”

Section: Analysis Of the Environmental Adaptability Of The Enhanced A...mentioning

confidence: 99%

“…Collision avoidance path planning is a fundamental technology in robotics and the foundation for robotic arm to complete complex work goals [1][2][3]. In recent years, a large number of heuristic algorithms [4][5][6] such as the genetic algorithm, neural network algorithm, particle swarm algorithm, and A * algorithm have been implemented. The genetic algorithm is an algorithm based on the evolution of biological populations that are widely used in path planning problems [7,8] due to its excellent real-time performance and global search capability.…”

Section: Introductionmentioning

confidence: 99%

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Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

Tang,

Zhou,

2023

Robotica

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Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

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Section: Analysis Of the Environmental Adaptability Of The Enhanced A...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

Tang,

Zhou,

2023

Robotica

View full text Add to dashboard Cite

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Global Path Planning for Unmanned Ships Based on Improved Particle Swarm Algorithm

Liu¹,

Liu²

2023

Communications in Computer and Information Science

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Multi-fidelity reduced-order model for GPU-enabled microfluidic concentration gradient design

Yang

Hong

Wang

et al. 2022

Engineering with Computers

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GPU-based Global Path Planning Using Genetic Algorithm with Near Corner Initialization

Cited by 11 publications

References 29 publications

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

Global Path Planning for Unmanned Ships Based on Improved Particle Swarm Algorithm

Multi-fidelity reduced-order model for GPU-enabled microfluidic concentration gradient design

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