Compliant Mechanism for Robotic In-Space Assembly and Learning-Based Safety Limit Detection

Autonomous in-space assembly requires mechanisms that can tolerate multi-axis misalignment without relying on active impedance control or large alignment features. This paper presents the design, fabrication, and experimental validation of a compact passive Remote Center of Compliance (RCC) device tailored for the constraints of in-space robotic assembly. The RCC employs space-qualified elastomeric elements arranged in a configuration to provide passive translational and rotational compliance, enabling correction of misalignment while maintaining low structural and control complexity. Bench-top experiments using a 7-DOF robotic arm demonstrate that the RCC accommodates lateral, pitch, and roll misalignments of up to 9 mm, 10°, and 14°, respectively, while reducing insertion forces and torques by 34-89% compared to a rigid end-effector. Because passive mechanisms provide no internal state feedback, we further develop a learning-based method for estimating RCC deflection and for detecting proximity to mechanical limits using short windows of force–torque and relative-pose data. Static stiffness and compliance models are shown to be insufficient due to strong nonlinearities and hysteresis, motivating a temporal convolutional network which infers full six-degree-of-freedom deflection. The model achieves normalized mean absolute errors of 0.14-0.18 and reliably classifies limit proximity using thresholded normalized displacement labels. Confusion matrices show high neutrality recall and strong directional consistency, with cross-sign errors below 10%, enabling conservative, low-latency limit detection suitable for real-time operation. Together, the proposed RCC device and learning-based safety-limit estimator provide a low-mass, low-power, and sensing-lightweight solution for misalignment-tolerant robotic assembly in the resource-constrained conditions of space environments.