FlexWorm: Primitive-augmented Hybrid Contact-motion Planning for Suction-based Multi-segment Deformable Robots
2026-08-17 • Robotics
Robotics
AI summaryⓘ
The authors developed a planning method to help multi-segment soft robots, which use suction to stick to surfaces, move better in complex 3D environments. Their approach figures out how to switch suction on and off and bend the robot's body while avoiding collisions and staying stable. They introduced two search methods to find good movement plans, with one using learned examples to speed up planning without losing success. Tests in simulations showed their methods worked better than older ones, and real robot trials confirmed the plans can be executed despite uncertainties. This helps make soft robots more autonomous and reliable for tasks in tight or delicate spaces.
soft robotsmulti-segment robotsuction adhesioninverse kinematicspath planning3D navigationcollision avoidancequasi-static feasibilityrobot motion primitiveshardware experiments
Authors
Zili Tang, Tiecheng Guo, Qinyue Zhang, Meng Guo
Abstract
Multi-segment suction-based soft robots are promising for inspection and maintenance in confined or fragile environments, but existing approaches still depend heavily on manually designed gaits and environment-specific motion scripts. This work presents a planning framework for serial multi-segment soft robots with deformable body segments and boundary suction pads. The formulation targets full 3D navigation on complex surfaces and explicitly handles discrete adhesion switching and continuous body deformation under geometric, collision, and quasi-static feasibility constraints, while remaining agnostic to the specific actuation realization used to produce segment deformation. Its core, block-wise IK hybrid search (IKHS), performs best-first search over feasible adhesion transitions while solving inverse kinematics only on induced free blocks. On top of IKHS, primitive-augmented hybrid search (PaHS) uses a learned observation--primitive embedding to retrieve short validated motion segments for fast local proposal, with fallback to standard IKHS branching when retrieval fails. In simulation, the framework consistently outperforms controlled baselines in planning success, transition quality, and efficiency across diverse terrains. PaHS matches IKHS in success rate while substantially reducing planning time. Repeated hardware experiments on a pneumatic multi-segment soft robot further demonstrate executability and online recovery under actuation and adhesion uncertainty.