Morphogenetic Assembly and Adaptive Control for Heterogeneous Modular Robots
Chongxi Meng, Da Zhao, Yifei Zhao, Minghao Zeng, Yanmin Zhou, Zhipeng Wang, Bin He
AI summary
Problem
Efficiently assembling heterogeneous modular robots into diverse configurations and providing them with immediate, morphology-agnostic adaptive control remains a significant challenge due to state-space explosion and complex dynamics.
Approach
The framework decouples assembly planning from motion execution using a hierarchical planner with type-penalized heuristics, while employing a GPU-accelerated annealing variance MPPI controller to generate real-time, morphology-agnostic locomotion policies.
Key results
- Hierarchical planner enables robust large-scale heterogeneous reconfiguration
- Type-penalized heuristics prevent symmetric deadlocks and ensure scalability
- Greedy heuristic reduces physical execution costs versus Hungarian heuristic
- GPU-accelerated annealing MPPI achieves 50 Hz real-time control with high tracking accuracy
Why it matters
This framework advances embodied intelligence by enabling autonomous construction and immediate adaptive operation of heterogeneous robotic systems for applications in space exploration and disaster response.
Abstract
This paper presents a closed-loop automation framework for heterogeneous modular robots, encompassing the entire pipeline from morphological construction to adaptive control. Within this framework, a mobile manipulator manip- ulates heterogeneous functional modules—including structural, joint, and wheeled modules—to dynamically assemble diverse robot configurations and grant them immediate locomotion capabilities. To address the state-space explosion inherent in large-scale heterogeneous reconfiguration, we propose a hierar- chical planner: the high-level planner employs a bi-directional heuristic search with type penalty terms to generate module- handling sequences, while the low-level planner utilizes A* search to compute optimal execution trajectories. This approach effectively decouples discrete configuration planning from con- tinuous motion execution. For adaptive motion generation of unknown assembled configurations, we introduce a GPU- accelerated Annealing Variance Model Predictive Path Integral (MPPI) controller. By incorporating a multi-stage variance annealing strategy to balance global exploration and local con- vergence, the controller achieves configuration-agnostic, real- time motion control. Large-scale simulations demonstrate that the type penalty term is crucial for planning robustness in heterogeneous scenarios. Furthermore, the greedy heuristic generates plans with lower physical execution costs compared to the Hungarian heuristic. The proposed Annealing-variance MPPI significantly outperforms standard MPPI in both velocity tracking accuracy and control frequency, achieving real-time control at 50 Hz. The framework successfully validates the full- cycle process, including module assembly, robot merging and splitting, and dynamic motion generation.