In this paper, we simultaneously tackle the problem of energy optimal and safe navigation of electric vehicles in a data-driven robust optimization framework. We consider a dynamic model of the electric vehicle which includes both longitudinal and lateral motion as well as dynamics of stored energy level. We leverage past data of obstacle motion to construct a future occupancy set with probabilistic guarantees, and formulate robust collision avoidance constraints with respect to such an occupancy set using convex programming duality. Consequently, we present the finite horizon optimal control problem subject to robust collision avoidance constraints while penalizing resulting energy consumption. Finally, we show the effectiveness of the proposed techniques in reducing energy consumption and ensuring safe navigation via extensive simulations.

翻译：本文在数据驱动鲁棒优化框架下，同时解决电动汽车能量最优与安全导航问题。考虑电动汽车动力学模型，该模型包含纵向运动、横向运动以及存储能量水平的动态变化。利用障碍物运动的历史数据构建具有概率保证的未来占据集，并借助凸规划对偶性，针对该占据集形式化鲁棒碰撞避免约束。在此基础上，提出受鲁棒碰撞避免约束的有限时域最优控制问题，并对产生的能量消耗施加惩罚。最后，通过大量仿真验证所提技术在降低能量消耗及保障安全导航方面的有效性。