DCAD: Decentralized Collision Avoidance with Dynamics Constraints for Agile Quadrotor Swarms

TL;DR

This paper introduces a decentralized collision avoidance algorithm for quadrotor swarms that ensures safety and smooth trajectories in dense environments by integrating ORCA, MPC, and uncertainty handling.

Contribution

It presents a novel decentralized collision avoidance method combining ORCA, flatness-based MPC, and Kalman filtering for quadrotor swarms in complex environments.

Findings

Superior trajectory smoothness compared to existing methods

Lower collision probability during high-speed maneuvers

Effective handling of downwash and sensing uncertainties

Abstract

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.