A RRT-A* Path Planning Algorithm for Cable-driven Manipulators

Zhang, Dong; Gai, Yan; Ju, Renjie; Miao, Zhiwen; Lao, Ju

doi:10.1109/robio55434.2022.10011890

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Various methods have been proposed, such as Jacobian pseudo-inverse methods [45][46][47], geometrical methods [48][49][50], and learning-based algorithms [51]. Offline planning algorithms, like RRT and A*, have also been utilized for CR path-planning tasks [52][53][54]. However, ADR missions are characterized by high levels of uncertainty and multiple time-varying factors.…”

Section: Introductionmentioning

confidence: 99%

A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots

Wang,

Sun,

Wang

et al. 2023

Sensors

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Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.

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