Multi-Robot Exploration
Overview
Multi-robot exploration studies how teams of robots can map unknown spaces, locate targets, or monitor changing environments while avoiding duplicated effort. Common approaches assign frontiers or viewpoints using information gain, then plan routes with methods such as graph search, market-based allocation, submodular optimization, or reinforcement learning. Teams may share maps and plans centrally or coordinate through local communication, observed teammate motion, or compact semantic messages, including when robots have different capabilities. A central challenge is balancing coverage and information quality against communication limits, energy, collision risk, and failures, while ensuring methods transfer from simulation to real robots.
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