Reconfigurable rover adapts shape for efficient obstacle climbing
GeoTrussRover: Morphological Computation with Contact-Semantic Control Primitives
RoboticsGraphicsNeural and Evolutionary Computing
Summary
Robots often struggle to climb over obstacles because their shapes can't easily change. The authors built a robot with a flexible truss structure on wheels, which lets it change shape to climb steps more easily. They found a way to break down its complex movements into simpler parts, so the robot can quickly adjust when facing new step heights without recalculating everything from scratch. Their robot prototype successfully climbed steps smaller than it was originally designed for, showing this method can save time and effort in controlling such complex robots.
What this means in practice
- •For field robotics engineers: Enable wheeled robots to adapt their body shape dynamically to climb various step heights without full motion replanning.
- •For robotics hardware developers: Incorporate variable-geometry truss structures into mobile robots to distribute load while supporting complex terrain negotiation.
Authors
Muyuan Ma, Yi Zhang, Yang Yang, Xuanyan Zheng, Ruiqi Hu, Boxuan Ke, Zhenyu Chen, Yicong Lin, Xin Hao Yang, Daliang Xiao, Zhinan Hou, Wanhao Niu, Yuan Sun, Yan Yang, Yue Xie
Abstract
Reconfigurable robots can change their contact geometry when a fixed body cannot negotiate an obstacle. A variable-geometry truss (VGT) distributes this shape change through a load-bearing structure, but coupling it to a mobile base creates a high-dimensional coordination problem. GeoTrussRover combines an electrically actuated VGT, a wheeled base, and contact-semantic morphology planning and control. We solve one source traversal and extract four contact-semantic primitives that describe coordination among 21 members. Physics-constrained projection adapts them to unseen step heights with the same contact topology. When every phase remains feasible, adaptation does not recompute the complete motion. If one phase violates the new physical constraints, only that phase is recomputed. A full-space QP then tracks the adapted motion and corrects member and wheel errors. For transfer from 0.10m to 0.075m, the method reduces objective-function evaluations by 63.7% relative to full recomputation. Contact-phase feasibility analysis covers step heights from 0.10 to 0.46m, or 1.08 to 4.97 wheel radii, with the upper value near the theoretical feasible boundary. The electric prototype traverses 2.11 wheel radii. The resulting low-dimensional representation stores task coordination in a hyper-redundant, load-bearing morphology and reuses it during locomotion.