论文标题
swarmcco：在不确定性下，四肢群的概率反应性碰撞避免
SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms under Uncertainty
论文作者
论文摘要
我们提出了在不确定状态估计下运行的四群群的去中心化碰撞算法。我们的方法利用了差异平坦的特性和前馈线性化，以近似四极管动力学并执行相互碰撞的回避。我们通过将碰撞约束作为机会约束来解释位置和速度的不确定性，这些约束描述了一组速度，这些速度避免了具有指定置信度的碰撞。我们提出了两种不同的方法来制定和解决机会限制：我们的第一种方法假设高斯噪声分布。我们的第二种方法是使用高斯混合模型（GMM）扩展到非高斯案例。我们将线性机会约束重新调整为同等的确定性约束，这些约束与MPC框架一起使用，以计算每个四型四型的无局部轨迹。我们在基准方案中的模拟中评估了所提出的算法，并突出了其对先前方法的好处。我们观察到，高斯和非高斯方法在确定性方法上都提供了改进的避免碰撞性能。平均而言，高斯方法需要约5毫秒来计算无局部无冲突的轨迹，而我们的非高斯方法在计算上更昂贵，在4个代理的情况下，平均需要约9ms。
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.
