Spatially distributed multiagent path planning

Abstract

Multiagent path planning is important in a variety of fields, ranging from games to robotics and warehouse management. Although centralized control in the joint action space can provide optimal plans, this often is computationally infeasible. Decoupled planning is much more scalable. Traditional decoupled approaches perform a unit-centric decomposition, replacing a multi-agent search with a series of single-agent searches, one for each mobile unit. We introduce an orthogonal, significantly different approach, following a spatial distribution that partitions a map into high-contention, bottleneck areas and low-contention areas. Local agents called controllers are in charge with one local area each, routing mobile units in their corresponding area. Distributing the knowledge across the map, each controller can observe only the state of its own area. Adjacent controllers can communicate to negotiate the transfer of mobile units. We evaluate our implemented algorithm, SDP, on real game maps with a mixture of larger areas and narrow, bottleneck gateways. The results demonstrate that spatially distributed planning can have substantial benefits in terms of makespan quality and computation speed.