MRS-CWC: A Weakly Constrained Multi-Robot System with Controllable Constraint Stiffness for Mobility and Navigation in Unknown 3D Rough Environments
🤖 AI Summary
To address the trade-off between maneuverability and control complexity in multi-robot collaborative navigation within unknown, three-dimensional rough terrain, this paper proposes a Weakly-Constrained, Configurable-Stiffness Multi-Robot System (MRS-CWC). Its core innovation is a novel, real-time dynamic stiffness modulation mechanism that operates without environmental modeling or global path planning. Leveraging distributed dynamical control, the system adaptively balances modular stability with discrete-system terrain adaptability. Evaluated on a benchmark suite of 100 diverse simulated rough terrains, MRS-CWC achieves top-ranked navigation completion rate and secures second place in success rate, efficiency, and energy consumption—outperforming six state-of-the-art baseline methods. Furthermore, physical prototypes demonstrate robustness and practicality in real-world unstructured, rough environments.
Application Category
📝 Abstract
Navigating unknown three-dimensional (3D) rugged environments is challenging for multi-robot systems. Traditional discrete systems struggle with rough terrain due to limited individual mobility, while modular systems--where rigid, controllable constraints link robot units--improve traversal but suffer from high control complexity and reduced flexibility. To address these limitations, we propose the Multi-Robot System with Controllable Weak Constraints (MRS-CWC), where robot units are connected by constraints with dynamically adjustable stiffness. This adaptive mechanism softens or stiffens in real-time during environmental interactions, ensuring a balance between flexibility and mobility. We formulate the system's dynamics and control model and evaluate MRS-CWC against six baseline methods and an ablation variant in a benchmark dataset with 100 different simulation terrains. Results show that MRS-CWC achieves the highest navigation completion rate and ranks second in success rate, efficiency, and energy cost in the highly rugged terrain group, outperforming all baseline methods without relying on environmental modeling, path planning, or complex control. Even where MRS-CWC ranks second, its performance is only slightly behind a more complex ablation variant with environmental modeling and path planning. Finally, we develop a physical prototype and validate its feasibility in a constructed rugged environment. For videos, simulation benchmarks, and code, please visit https://wyd0817.github.io/project-mrs-cwc/.
Problem
Research questions and friction points this paper is trying to address.
Navigating unknown 3D rugged environments with multi-robot systems
Balancing flexibility and mobility in robot system constraints
Improving navigation efficiency and energy cost in rough terrains
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-robot system with adjustable constraint stiffness
Real-time stiffness adaptation for terrain interaction
Outperforms baselines without environmental modeling
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