Improved Manipulator Obstacle Avoidance Path Planning Based on Potential Field Method

Zhao, Ming; Lv, Xiaoqing

doi:10.1155/2020/1701943

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…The trajectory obtained by this method can be smooth and safe. Zhao et al [ 28 ] established dynamic virtual target points to enhance the predictive ability of the manipulator to the road ahead. Xu et al [ 29 ] proposed a saturated function in the attractive velocity potential field algorithm, which slows down to the goal progressively.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method

Xia

Sang

et al. 2023

Sensors

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In medical and surgical scenarios, the trajectory planning of a collaborative robot arm is a difficult problem. The artificial potential field (APF) algorithm is a classic method for robot trajectory planning, which has the characteristics of good real-time performance and low computing consumption. There are many variants of the APF algorithm, among which the most widely used variants is the velocity potential field (VPF) algorithm. However, the traditional VPF algorithm has inherent defects and problems, such as easily falling into local minimum, being unable to reach the target, poor dynamic obstacle avoidance ability, and safety and efficiency problems. Therefore, this work presents the improved velocity potential field (IVPF) algorithm, which considers direction factors, obstacle velocity factor, and tangential velocity. When encountering dynamic obstacles, the IVPF algorithm can avoid obstacles better to ensure the safety of both the human and robot arm. The IVPF algorithm also does not easily fall into a local problem when encountering different obstacles. The experiments informed the RRT* algorithm, VPF algorithm, and IVPF algorithm for comparison. Compared with the informed RRT* and VPF algorithm, the result of experiments indicate that the performances of the IVPF algorithm have significant improvements when dealing with different obstacles. The main aim of this paper is to provide a safe and efficient path planning algorithm for the robot arm in the medical field. The proposed algorithm can ensure the safety of both the human and the robot arm when the medical and surgical robot arm is working, and enables the robot arm to cope with emergencies and perform tasks better. The application of the proposed algorithm could make the collaborative robots work in a flexible and safe condition, which could open up new opportunities for the future development of medical and surgical scenarios.

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Section: Introductionmentioning

confidence: 99%

Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method

Xia

Sang

et al. 2023

Sensors

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“…Specifically, the artificial potential field method is extensively used in mechanical arm obstacle avoidance path planning because of its advantages of efficient operation and simple configuration, even though the traditional version of this method shows defects of easily falling into local extremes and easy oscillation around obstacles. To address these defects, the research presented in the study of Zhao and Lv (2020) jumped out of the local minima by establishing a virtual target point and then adding a small displacement at the end of the mechanical arm, which produced an algorithm non-applicable to more complex obstacle environments. The study in Zhou et al (2020) performs path planning by changing the repulsive potential field function and adding sub-target points.…”

Section: Introductionmentioning

confidence: 99%

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

Zhou

Tan³

et al. 2021

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Purpose This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process. Design/methodology/approach In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point. Findings Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability. Originality/value An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

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“…One of the real-time methods is Artificial Potential Field (APF) [24] [25]. Khatib was the first to introduce the APF method for manipulators and mobile robots [26][27] [28]. The APF can be implemented in mobile robots [29][30] [31], flying robots [32], manipulator robots [33] [34], and humanoid robot [35].…”

Section: Introductionmentioning

confidence: 99%

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

Ma’arif

Rahmaniar

Márquez-Vera

et al. 2021

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)

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Artificial potential field (APF) is the effective realtime guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve oneobstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON) and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems.

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Improved Manipulator Obstacle Avoidance Path Planning Based on Potential Field Method

Cited by 15 publications

References 11 publications

Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method

Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

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