  Collision-free navigation in cluttered environments with static and dynamic
obstacles is essential for many multi-robot tasks. Dynamic obstacles may also
be interactive, i.e., their behavior varies based on the behavior of other
entities. We propose a novel representation for interactive behavior of dynamic
obstacles and a decentralized real-time multi-robot trajectory planning
algorithm allowing inter-robot collision avoidance as well as static and
dynamic obstacle avoidance. Our planner simulates the behavior of dynamic
obstacles, accounting for interactivity. We account for the perception
inaccuracy of static and prediction inaccuracy of dynamic obstacles. We handle
asynchronous planning between teammates and message delays, drops, and
re-orderings. We evaluate our algorithm in simulations using 25400 random cases
and compare it against three state-of-the-art baselines using 2100 random
cases. Our algorithm achieves up to 1.68x success rate using as low as 0.28x
time in single-robot, and up to 2.15x success rate using as low as 0.36x time
in multi-robot cases compared to the best baseline. We implement our planner on
real quadrotors to show its real-world applicability.
