Multi-Robot Object Transport in Constrained Environments: A Model Predictive Control Approach

Abstract

This paper presents a robust approach for cooperative object transportation using a multi-robot system driven by Model Predictive Control (MPC). Object transportation is a critical task in various applications such as industrial automation, logistics, and search and rescue, requiring efficient and reliable methods to handle dynamic and constrained environments. Utilizing multiple robots in object transportation enhances flexibility, scalability, and efficiency, allowing for distributed workload and improved robustness against individual robot failures. Current techniques in multi-robot object transportation include centralized and decentralized control strategies, The key challenges are communication constraints, coordination complexity, and real-time adaptability. Our methodology leverages Decentralized MPC, which significantly contributes to overcoming these challenges by enabling real-time, local decision-making while ensuring global coordination and optimal performance. The proposed approach is proven through various types of scenarios, demonstrating its potential as a scalable and efficient solution for multi-robot systems operating in constrained environments.