Work

Redundancy-Based Motion Planning with Task Constraints for Robot Manipulators

Year: 2025

Type: article

Abstract: Finding realistic motions for redundant manipulators is essential for complex jobs such as home care and industrial assembly. Motion planning is complex when a task requires standing upright or moving through restricted spaces. This work provides an effective motion-planning strategy for 7-DOF manipulators that improves connections via redundancy. The analytic Cartesian-space-to-joint-space kinematic mapping models for 7-DOF redundant manipulators with diverse configurations are constructed first, and the feasible nodes are determined by sampling the Cartesian space without barriers to satisfy the task requirements. Each Cartesian-space sampling node can provide numerous feasible joint-space nodes because of the redundancy of the robot manipulators. To remove additional valid nodes from a singular position, joint configurations with the same end-effector position orientation are modified iteratively. Finally, we find the nearest nodes in the joint-space constraint manifold and build collision-free smooth pathways. The task constraint levels were varied for a 7-DOF manipulator in simulations and experiments. The proposed planner finds more viable nodes at the same end-position attitude than one-to-one projection. It does not require numerical iterations and achieves high planning efficiency and a high motion-planning success rate.

Source: Sensors

DOI: 10.3390/s25061900

Authors: Yi Zhang, Hongguang Wang

Institutions: Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, State Key Laboratory of Robotics

Language: English

Topic: Robotic Mechanisms and Dynamics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China