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.
翻译:我们提出了避免自发物剂和动态障碍之间碰撞的模型预测控制(MPC),这种模型预测控制(MPC)是针对自动物剂和动态障碍(预测不确定)之间避免碰撞的。避免碰撞的制约是通过在代表物剂和障碍的锥形体之间保持正距离而施加的,并且使用拉格朗的双重性来进行可移动的重新校正。这一方法可以使避免碰撞的制约(即使是多面体,否则就需要混合内聚物或非隔热性制约)顺利地得到顺利。我们考虑了关于不确定障碍位置的三个广泛使用的描述:(1) 带有多面支持的任意分布;(2) 高斯分布和(3) 任意分布,最初两分钟已经知道。对于每一个案例,我们获得了避免碰撞限制的确定性重新配方。拟议的多面体能控制(MPC)的提法优化了在满足避免碰撞的制约方面减少保守主义的反馈政策。我们用CARLA的交通交叉点模拟对拟议方法进行了验证。