Morphogenetic Assembly and Adaptive Control for Heterogeneous Modular Robots

TL;DR

This paper introduces a comprehensive framework for the automated assembly and adaptive control of heterogeneous modular robots, enabling dynamic configuration, planning, and real-time motion control through hierarchical planning and GPU-accelerated algorithms.

Contribution

It presents a novel integrated system combining hierarchical planning and GPU-accelerated adaptive control for heterogeneous modular robots, addressing large-scale reconfiguration and real-time motion generation.

Findings

Type-penalty improves planning robustness in heterogeneous scenarios.

Greedy heuristic reduces physical execution costs.

Annealing-Variance MPPI outperforms standard MPPI in accuracy and control frequency.

Abstract

This paper presents a closed-loop automation framework for heterogeneous modular robots, covering the full pipeline from morphological construction to adaptive control. In this framework, a mobile manipulator handles heterogeneous functional modules including structural, joint, and wheeled modules to dynamically assemble diverse robot configurations and provide them with immediate locomotion capability. To address the state-space explosion in large-scale heterogeneous reconfiguration, we propose a hierarchical planner: the high-level planner uses a bidirectional heuristic search with type-penalty terms to generate module-handling sequences, while the low level planner employs A* search to compute optimal execution trajectories. This design 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 enables configuration-agnostic, real-time motion control. Large scale simulations show that the type-penalty term is critical for planning robustness in heterogeneous scenarios. Moreover, the greedy heuristic produces plans with lower physical execution costs than 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 validates the full-cycle process, including module assembly, robot merging and splitting, and dynamic motion generation.