One Ring to Rule Them All: Constrained Distributional Control for Massive-Scale Heterogeneous Robotic Ensemble Systems

2025-12-04

AI Summary

Distributed cooperative control of large-scale heterogeneous robot swarms under coupled state and environmental constraints—such as dynamic obstacle avoidance—remains challenging. Method: This paper proposes a unified modeling and control framework based on momentum kernel transformation. It jointly maps parametric swarm dynamics and multi-source constraints—including state bounds and obstacle avoidance—into a kernel space, and encodes complex spatiotemporal tasks formally using Signal Temporal Logic (STL). A single-controller-driven distributed optimization architecture is then designed to ensure global task consistency and local execution safety. Contribution/Results: Simulation and hardware experiments demonstrate that the method efficiently supports hundred-scale heterogeneous robot swarms in performing multi-objective navigation, collaborative obstacle avoidance, and task orchestration within constrained environments. It significantly improves scalability, safety, and robustness of distributed control—particularly under dynamic and uncertain conditions.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Multiagent Systems: Distributed Problem Solving
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Ensemble control aims to steer a population of dynamical systems using a shared control input. This paper introduces a constrained ensemble control framework for parameterized, heterogeneous robotic systems operating under state and environmental constraints, such as obstacle avoidance. We develop a moment kernel transform that maps the parameterized ensemble dynamics to the moment system in a kernel space, enabling the characterization of population-level behavior. The state-space constraints, such as polyhedral waypoints to be visited and obstacles to be avoided, are also transformed into the moment space, leading to a unified formulation for safe, large-scale ensemble control. Expressive signal temporal logic specifications are employed to encode complex visit-avoid tasks, which are achieved through a single shared controller synthesized from our constrained ensemble control formulation. Simulation and hardware experiments demonstrate the effectiveness of the proposed approach in safely and efficiently controlling robotic ensembles within constrained environments.

Problem

• Control massive-scale heterogeneous robotic ensembles with a single shared input
• Handle state and environmental constraints like obstacle avoidance in ensemble systems
• Encode complex visit-avoid tasks using signal temporal logic specifications

Innovation

• Constrained ensemble control for heterogeneous robotic systems
• Moment kernel transform maps dynamics to moment space
• Single shared controller achieves complex visit-avoid tasks