Morphogenetic Assembly and Adaptive Control for Heterogeneous Modular Robots

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 manipulates 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 hierarchical 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 continuous 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 convergence, 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.