Cooperative distributed model predictive control for embedded systems: Experiments with hovercraft formations

2024-09-20

arXiv.org

AI Summary

This work addresses the decentralized embedded formation control of multiple hovercrafts without centralized coordination. We propose a stabilization-oriented, real-time distributed model predictive control (DMPC) framework leveraging the alternating direction method of multipliers (ADMM) integrated with embedded real-time optimization. Implemented on an air-hockey experimental platform, the approach achieves millisecond-level closed-loop control entirely onboard—no central coordinator is required. Our primary contribution is the first experimental validation of ADMM-based DMPC on a real embedded hovercraft system for simultaneous dynamic obstacle avoidance and high-precision trajectory tracking, while concurrently ensuring formation maintenance, point-to-point navigation, and inter-agent collision avoidance. Experimental results demonstrate that the framework delivers real-time performance, robustness against disturbances, and scalability under severe computational resource constraints.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling step via a stabilizing decentralized real-time iteration scheme using the alternating direction method of multipliers. The efficient implementation does not require a central coordinator, executes onboard the hovercraft, and facilitates sampling intervals in the millisecond range. The formation control experiments showcase the flexibility of the approach on scenarios with point-to-point transitions, trajectory tracking, collision avoidance, and moving obstacles.

Problem

• Implement cooperative distributed model predictive control for hovercraft formations.
• Solve centralized optimal control problems without a central coordinator.
• Achieve real-time control with millisecond sampling intervals onboard hovercraft.

Innovation

• Decentralized real-time iteration scheme
• Alternating direction method of multipliers
• Onboard execution without central coordinator