Multi-Robot Task Allocation
Overview
Multi-robot task allocation studies how teams divide tasks so robots with different abilities can complete missions efficiently, safely, and reliably. Common approaches include auctions and consensus for distributed assignment, matching and mathematical optimization for structured problems, and reinforcement learning or graph-based policies for dynamic settings. In many applications, allocation is coupled with routing, path planning, scheduling, communication, and constraints such as battery life or shared resources. A central challenge is balancing solution quality with scalability and responsiveness, especially when robots have partial information, unreliable communication, or changing tasks.
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