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.

翻译：本文提出了一种面向异构模块化机器人的闭环自动化框架，覆盖从形态构建到自适应控制的全流程。在该框架中，一个移动机械臂操作包括结构模块、关节模块和轮式模块在内的异构功能模块，动态组装出多种机器人构型，并为其提供即时运动能力。为解决大规模异构重构中的状态空间爆炸问题，我们提出了一种分层规划器：高层规划器采用带类型惩罚项的双向启发式搜索生成模块操作序列，而底层规划器使用A*搜索计算最优执行轨迹。该设计有效解耦了离散构型规划与连续运动执行。针对未知组装构型的自适应运动生成，我们引入了一种GPU加速的退火方差模型预测路径积分（MPPI）控制器。通过采用多阶段方差退火策略来平衡全局探索与局部收敛，该控制器实现了与构型无关的实时运动控制。大规模仿真表明，类型惩罚项对于异构场景下的规划鲁棒性至关重要。此外，贪婪启发式算法生成的方案比匈牙利启发式算法具有更低的物理执行成本。所提出的退火方差MPPI在速度跟踪精度和控制频率上均显著优于标准MPPI，实现了50 Hz的实时控制。该框架验证了包括模块组装、机器人合并与拆分以及动态运动生成在内的全周期流程。