Work

CP-QRRT*: A Path Planning Algorithm for Hyper-Redundant Manipulators Considering Joint Angle Constraints

Year: 2025

Type: article

Abstract: A novel algorithm (CP-QRRT*) is proposed for the path planning tasks of hyper-redundant manipulators (HRMs) in confined spaces, addressing the issues of unmet joint angle constraints, redundant planning paths, and long planning times present in previous algorithms. First, the PSO algorithm is introduced to optimize the random sampling process of the RRT series algorithms, enhancing the directionality of the random tree expansion. Subsequently, the method of backtracking ancestor nodes from the Quick-RRT* algorithm is combined to avoid getting trapped in local optima. Finally, a constraint module designed based on the maximum joint angle constraints of the HRM is implemented to limit the path deflection angles. Simulation experiments demonstrate that the proposed algorithm can satisfy the joint angle constraints of the HRM, and the planned paths are shorter and require less time.

Source: Sensors

DOI: 10.3390/s25051490

Authors: Tianya Wang, Guoliang Ma, Lisong Xu, Rui Yu

Institution: Nanjing University of Science and Technology

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Jiangsu Agricultural Science and Technology Independent Innovation Fund