Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task

Abstract

Wire-harnessing tasks pose great challenges to be automated by the robot due to the complex dynamics and unpredictable behavior of the deformable wire. Traditional methods, often reliant on dual-robot arms or tactile sensing, face limitations in adaptability, cost, and scalability. This paper introduces a novel single-robot wire-harnessing pipeline that leverages a robot's twisting motion to generate necessary wire tension for precise insertion into clamps, using only one robot arm with an integrated force/torque (F/T) sensor. Benefiting from this design, the single robot arm can efficiently apply tension for wire routing and insertion into clamps in a narrow space. Our approach is structured around four principal components: a Model Predictive Control (MPC) based on the Koopman operator for tension tracking and wire following, a motion planner for sequencing harnessing waypoints, a suite of insertion primitives for clamp engagement, and a fix-point switching mechanism for wire constraint updating. Evaluated on an industrial-level wire harnessing task, our method demonstrated superior performance and reliability over conventional approaches, efficiently handling both single and multiple wire configurations with high success rates.

Figures (7)

• Figure 1: NIST task board setup for wire harnessing task. We use a 3D-printed finger with grooves (orange box) to harness the wire into "C"-shaped (blue box) and "U"-shaped clamps (red box).
• Figure 2: State space of the wire harnessing task. $O$ represents the fixed wire origin. $E$ is the robot end-effector (gripper) to bend and twist the wire. The state space consists of $(x,y,\theta,f)$ denotes the robot position and rotation relative to $O$ and the tension force. $\phi$ is the twisting angle between the wire and gripper.
• Figure 3: The overview of our proposed approach: a) the pipeline for wire-harnessing, b) harnessing motion planer that sequences and merges a set of clamp-centric waypoints for each of the clamps, c) Top: We use pre-scripted motions to collect real-world wire following data, then augment by 10 times for Koopman operator fitting. Bottom: We first process the waypoints and robot state in the Cartesian space to the fix-point frame and use Koopman lift function to obtain the wire state. Then we utilize MPC to infer the optimal control command.
• Figure 4: Insertion primitives for "C"-shaped clamps (top) and "U"-shaped clamps
• Figure 5: Fix-point switching mechanism.