DCAD: Decentralized Collision Avoidance with Dynamics Constraints for Agile Quadrotor Swarms
This addresses safety and efficiency for quadrotor swarm navigation in cluttered settings, representing an incremental improvement over existing approaches.
The paper tackles decentralized collision avoidance for quadrotor swarms in dense environments, resulting in smoother trajectories and lower collision probability during high-speed maneuvers compared to state-of-the-art methods.
We present a novel, decentralized collision avoidance algorithm for navigating a swarm of quadrotors in dense environments populated with static and dynamic obstacles. Our algorithm relies on the concept of Optimal Reciprocal CollisionAvoidance (ORCA) and utilizes a flatness-based Model Predictive Control (MPC) to generate local collision-free trajectories for each quadrotor. We feedforward linearize the non-linear dynamics of the quadrotor and subsequently use this linearized model in our MPC framework. Our method is downwash conscious and computes safe trajectories that avoid quadrotors from entering each other's downwash regions during close proximity maneuvers. In addition, we account for the uncertainty in sensed position and velocity data using Kalman filtering. We evaluate the performance of our algorithm with other state-of-the-art methods and demonstrate its superior performance in terms of smoothness of generated trajectories and lower probability of collision during high velocity maneuvers.