Stiffness-Guided Adaptive Path Planning for Cable-Driven Dual-Arm Manipulators

Owing to their lightweight design and structural flexibility, cable-driven dual-arm manipulators offer significant advantages in collaborative tasks. However, practical applications pose several challenges. The existence of environmental obstacles and the closed kinematic chain considerably increase the complexity of path planning. Furthermore, the inherently low stiffness of cable-driven systems can result in end-effector deformation, thereby reducing precision during path-following tasks. This paper proposes an adaptive, sampling-based path planning algorithm that addresses obstacle avoidance and closed kinematic chain constraint, facilitating efficient path planning in diverse operational environments. Additionally, an evaluation metric for the Cartesian stiffness of cable-driven dual-arm manipulators is introduced and incorporated into the proposed algorithm for optimization. This approach enhances both Cartesian stiffness and end-effector precision, thereby improving the reliability and accuracy of cable-driven dual-arm manipulators in practical applications.