SwarmCCO: 不確実性下のクアッドロータ群のための確率的リアクティブ衝突回避
SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms under Uncertainty
状態推定に不確実性があるクアッドロータ群に対し、位置・速度の不確かさをチャンス制約として定式化し、MPCで各機の衝突回避軌道を分散計算する手法を提案。ガウス分布とGMM拡張の2手法を比較し、決定論的手法より衝突回避性能が向上することを示した。
著者: Senthil Hariharan Arul, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and performs reciprocal collision avoidance. We account for the uncertainty in position and velocity by formulating the collision constraints as chance constraints, which describe a set of velocities that avoid collisions with a specified confidence level. We present two different methods for formulating and solving the chance constraints: our first method assumes a Gaussian noise distribution. Our second method is its extension to the non-Gaussian case by using a Gaussian Mixture Model (GMM). We reformulate the linear chance constraints into equivalent deterministic constraints, which are used with an MPC framework to compute a local collision-free trajectory for each quadrotor. We evaluate the proposed algorithm in simulations on benchmark scenarios and highlight its benefits over prior methods. We observe that both the Gaussian and non-Gaussian methods provide improved collision avoidance performance over the deterministic method. On average, the Gaussian method requires ~5ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~9ms on average in scenarios with 4 agents.
