Hierarchically Consistent Motion Primitives for Quadrotor Coordination
This work addresses scalable and robust coordination for multi-agent systems like quadrotors, but appears incremental as it builds on existing hierarchical planning methods.
The authors tackled the problem of motion planning for large multi-agent systems by introducing a hierarchical framework that constructs motion primitives from low-level to high-level, and demonstrated its effectiveness on quadrotors navigating cluttered environments while maintaining formation.
We present a hierarchical framework for motion planning of a large collection of agents. The proposed framework starts from low level motion primitives over a gridded workspace and provides a set of rules for constructing higher level motion primitives. Our hierarchical approach is highly scalable and robust making it an ideal tool for planning for multi-agent systems. Results are demonstrated experimentally on a collection of quadrotors that must navigate a cluttered environment while maintaining a formation.