Distributed Model Predictive Covariance Steering

2022-12-01

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

This paper addresses safe, decentralized cooperative control of large-scale multi-robot systems under stochastic uncertainty. Method: We propose a provably safe, decentralized, and scalable receding-horizon covariance-guided framework. It integrates Wasserstein-distance-guided state distribution optimization, disturbance-feedback policy parameterization, and a distributed ADMM consensus algorithm, combined with probabilistic constraint approximation and model predictive control to jointly regulate both the mean and covariance of robot state distributions. Contribution/Results: The approach provides theoretical guarantees for collision avoidance and recursive feasibility, with computation scaling linearly in the number of robots. In simulations involving up to 100 robots, it significantly outperforms existing stochastic MPC methods in terms of real-time performance and robustness. Furthermore, it has been successfully deployed on a physical multi-robot hardware platform, demonstrating both efficacy and engineering practicality.

Abstract

This paper proposes Distributed Model Predictive Covariance Steering (DiMPCS) for multi-agent control under stochastic uncertainty. The scope of our approach is to blend covariance steering theory, distributed optimization and model predictive control (MPC) into a single framework that is safe, scalable and decentralized. Initially, we pose a problem formulation that uses the Wasserstein distance to steer the state distributions of a multi-agent system to desired targets, and probabilistic constraints to ensure safety. We then transform this problem into a finite-dimensional optimization one by utilizing a disturbance feedback policy parametrization for covariance steering and a tractable approximation of the safety constraints. To solve the latter problem, we derive a decentralized consensus-based algorithm using the Alternating Direction Method of Multipliers. This method is then extended to a receding horizon form, which yields the proposed DiMPCS algorithm. Simulation experiments on a variety of multi-robot tasks with up to hundreds of robots demonstrate the effectiveness of DiMPCS. The superior scalability and performance of the proposed method is also highlighted through a comparison against related stochastic MPC approaches. Finally, hardware results on a multi-robot platform also verify the applicability of DiMPCS on real systems.

Problem

• Multi-robot control
• Collision avoidance
• Uncertain environments

Innovation

• DiMPCS
• Wasserstein distance
• Autonomous decision-making