Abstract
This paper presents an effective multi-robot motion planner that
enables each robot to reach its desired location while avoiding
collisions with the other robots and the obstacles. The approach takes
into account the differential constraints imposed by the underlying
dynamics of each robot and generates dynamically-feasible motions that
can be executed in the physical world. The crux of the approach is
the sampling-based expansion of a motion tree in the continuous state
space of all the robots guided by multi-agent search over a discrete
abstraction. Experiments using vehicle models with nonlinear dynamics
operating in complex environments show significant speedups over
related work.
Keywords
- Agent-based and Multi-agent Systems: Multi-agent Planning
- Robotics: Motion and Path Planning
