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We present decentralized collision avoidance algorithms for quadrotor swarmsoperating under uncertain state estimation. Our approach exploits thedifferential flatness property and feedforward linearization to approximate thequadrotor dynamics and performs reciprocal collision avoidance. We account forthe uncertainty in position and velocity by formulating the collisionconstraints as chance constraints, which describe a set of velocities thatavoid collisions with a specified confidence level. We present two differentmethods for formulating and solving the chance constraints: our first methodassumes a Gaussian noise distribution. Our second method is its extension tothe non-Gaussian case by using a Gaussian Mixture Model (GMM). We reformulatethe linear chance constraints into equivalent deterministic constraints, whichare used with an MPC framework to compute a local collision-free trajectory foreach quadrotor. We evaluate the proposed algorithm in simulations on benchmarkscenarios and highlight its benefits over prior methods. We observe that boththe Gaussian and non-Gaussian methods provide improved collision avoidanceperformance over the deterministic method. On average, the Gaussian methodrequires ~5ms to compute a local collision-free trajectory, while ournon-Gaussian method is computationally more expensive and requires ~9ms onaverage in scenarios with 4 agents.

SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms under Uncertainty