Safe multi-agent motion planning under uncertainty for drones using
  filtered reinforcement learning
Sleiman Safaoui Abraham P. Vinod Ankush Chakrabarty Rien Quirynen Nobuyuki Yoshikawa Stefano Di Cairano
We consider the problem of safe multi-agent motion planning for drones in
uncertain, cluttered workspaces. For this problem, we present a tractable
motion planner that builds upon the strengths of reinforcement learning and
constrained-control-based trajectory planning. First, we use single-agent
reinforcement learning to learn motion plans from data that reach the target
but may not be collision-free. Next, we use a convex optimization, chance
constraints, and set-based methods for constrained control to ensure safety,
despite the uncertainty in the workspace, agent motion, and sensing. The
proposed approach can handle state and control constraints on the agents, and
enforce collision avoidance among themselves and with static obstacles in the
workspace with high probability. The proposed approach yields a safe, real-time
implementable, multi-agent motion planner that is simpler to train than methods
based solely on learning. Numerical simulations and experiments show the
efficacy of the approach.