cs.ROOct 29, 2026

CuBot-STT: Simultaneously Transport and Tighten for Lattice-type Modular Reconfiguration Robot

Authors: Chongxi Meng, Da Zhao, Yanmin Zhou, Runjie Shen, Zhipeng Wang, Bin He

Organizations: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China · Department of Mechanical and Automation Engineering and T Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong · Department of Control Science and Engineering, Tongji University, Shanghai, China

Abstract

Lattice-type modular reconfigurable robots rearrange standardized modules across tasks. Dedicated transport robots reduce module weight and cost by eliminating module level reconfiguration actuators. But existing systems use low capacity connections or internal fastening robots that occupy space for functional components. This paper presents CuBot-STT, which integrates module transport, interface locking, and release in one external robot. Cubic modules provide structural support and 24 V24\,\mathrm{V} power distribution. On each face, a gear transmission converts one external input into motion of four helical locking points, preserving internal space. The robot uses alternating foot support, a gripper for module handling, and a tightening head for interface actuation. The controller coordinates two-foot support and module locking through continuous constraint transfers. The planner decomposes relocation costs into an analytical lower bound and a residual learned through expert-guided, goal conditioned Q-learning. Contact face A∗A^* search expands relocation sequences into robot primitives satisfying connectivity, asymmetric manipulation, and foot stance constraints. Experiments demonstrate normal and tangential interface capacities of 300 N300\,\mathrm{N} and 490 N490\,\mathrm{N}, electrical continuity across six modules, and autonomous reconfiguration of an 11-module lattice. Planning benchmarks show superior search efficiency over analytical guidance and bidirectional heuristic search, with generalization beyond training lattice sizes without fine-tuning.

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