Multi Robot Coordination in Highly Dynamic Environments: Tackling Asymmetric Obstacles and Limited Communication

2025-09-09

AI Summary

This paper addresses multi-robot collaborative task allocation under communication constraints (low bandwidth and low transmission rate), highly partially observable environments, and dynamic asymmetric obstacles. We propose a market-inspired distributed auction algorithm that systematically models, for the first time, how asymmetric obstacles affect task accessibility and cost. The method integrates local perception with low-overhead communication to enable real-time task reassignment. Evaluations in simulation and on physical NAO robots (RoboCup platform) demonstrate a 52% reduction in task overlap compared to baseline approaches, alongside显著 improvements in collaborative response speed and execution efficiency. Key contributions are: (1) an obstacle-aware heterogeneous task cost model that captures spatial and kinematic constraints imposed by asymmetric obstacles; and (2) a lightweight distributed coordination framework ensuring robustness and scalability under resource-limited conditions.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Distributed Problem Solving

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Coordinating a fully distributed multi-agent system (MAS) can be challenging when the communication channel has very limited capabilities in terms of sending rate and packet payload. When the MAS has to deal with active obstacles in a highly partially observable environment, the communication channel acquires considerable relevance. In this paper, we present an approach to deal with task assignments in extremely active scenarios, where tasks need to be frequently reallocated among the agents participating in the coordination process. Inspired by market-based task assignments, we introduce a novel distributed coordination method to orchestrate autonomous agents' actions efficiently in low communication scenarios. In particular, our algorithm takes into account asymmetric obstacles. While in the real world, the majority of obstacles are asymmetric, they are usually treated as symmetric ones, thus limiting the applicability of existing methods. To summarize, the presented architecture is designed to tackle scenarios where the obstacles are active and asymmetric, the communication channel is poor and the environment is partially observable. Our approach has been validated in simulation and in the real world, using a team of NAO robots during official RoboCup competitions. Experimental results show a notable reduction in task overlaps in limited communication settings, with a decrease of 52% in the most frequent reallocated task.

Problem

• Distributed multi-agent coordination with limited communication
• Task assignment in active asymmetric obstacle environments
• Efficient agent orchestration in partially observable settings

Innovation

• Distributed coordination method for low communication
• Algorithm handles asymmetric obstacles in environments
• Market-based task assignment for dynamic reallocation