Year: 2026
Type: article
Abstract: For the clinical demand of sequential needling at multiple facial acupoints, needles already inserted and retained in the tissue become static obstacles to subsequent insertions. Planning a collision-free, smooth and efficient insertion path for each new needle inside the narrow facial workspace is therefore a key problem in the automation of acupuncture robots. This paper takes an acupuncture robot system composed of a UR5e six-degree-of-freedom manipulator and a self-developed acupuncture end effector as the research object, formulates multi-acupoint sequential needling as a constrained motion planning problem in which the obstacle set grows with the needling sequence, and proposes an improved APF-RRT* algorithm. The algorithm combines a bidirectional growth mechanism and goal-biased sampling on the RRT* framework to improve the convergence of the two trees, introduces Informed ellipsoidal subset sampling to reduce ineffective exploration, constructs an improved artificial potential field to alleviate the goal-unreachable and local-minimum problems, and designs a dynamic adaptive step-size strategy based on the ratio of attractive to repulsive force together with a bidirectional greedy path-pruning strategy. Simulation results in two- and three-dimensional environments show that, under the same maximum number of nodes, the proposed algorithm converges faster and yields a lower path cost than RRT* and APF-RRT*: in the 2D environment the runtime is reduced from 44.702 s to 9.538 s and the path cost from 792.702 to 750.477, while in the 3D environment the runtime is reduced from 84.594 s to 32.796 s and the path cost from 933.077 to 822.857, verifying the effectiveness and engineering applicability of the algorithm.
DOI: 10.61784/jcsee3160
Authors: Yi Wang, DianHao Wu, ZiChen Ma, KunLin Xie, MengDi Wang
Institution: University of Science and Technology Liaoning
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7223186318