论文标题
避免使用模型预测控制的动态障碍物的碰撞，并具有不确定的预测
Collision Avoidance for Dynamic Obstacles with Uncertain Predictions using Model Predictive Control
论文作者
论文摘要
我们提出了一个模型预测控制（MPC），以避免自主药物和动态障碍之间的碰撞，并具有不确定的预测。避免碰撞的限制是通过在代表代理和障碍物的凸组之间执行正距离的，并使用拉格朗日二元性谨慎地对其进行了重新校正。这种方法即使对于多面体，否则就需要混合构成或非平滑限制的多型，也可以平稳避免碰撞约束。我们考虑了不确定障碍位置的三种广泛使用的描述：1）具有多重支持的任意分布，2）高斯分布和3）任意分布，并以已知的前两个矩。对于每种情况，我们都会获得避免碰撞限制的确定性重新纠正。拟议的MPC公式优化了反馈政策，以减少满足碰撞避免限制的保守主义。使用Carla中交通交叉点的模拟对所提出的方法进行了验证。
We propose a Model Predictive Control (MPC) for collision avoidance between an autonomous agent and dynamic obstacles with uncertain predictions. The collision avoidance constraints are imposed by enforcing positive distance between convex sets representing the agent and the obstacles, and tractably reformulating them using Lagrange duality. This approach allows for smooth collision avoidance constraints even for polytopes, which otherwise require mixed-integer or non-smooth constraints. We consider three widely used descriptions of the uncertain obstacle position: 1) Arbitrary distribution with polytopic support, 2) Gaussian distributions and 3) Arbitrary distribution with first two moments known. For each case we obtain deterministic reformulations of the collision avoidance constraints. The proposed MPC formulation optimizes over feedback policies to reduce conservatism in satisfying the collision avoidance constraints. The proposed approach is validated using simulations of traffic intersections in CARLA.
