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 kinematic variables in both inertial and body coordinate systems in order to capture both longitudinal and lateral motion as well as state-of-energy of the battery. 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 approach in reducing energy consumption and ensuring safe navigation via extensive simulations involving curved roads and multiple obstacles.

翻译：本文在数据驱动的鲁棒优化框架下同时解决电动汽车节能最优与安全导航问题。我们建立了包含惯性系与车身坐标系运动学变量的电动汽车动力学模型，以同时表征纵向与横向运动及电池能量状态。通过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合，并利用凸规划对偶性推导出针对该占据集合的鲁棒避碰约束。由此提出了受鲁棒避碰约束的有限时域最优控制问题，同时引入能量消耗惩罚项。最后，通过包含弯道与多障碍物场景的仿真实验，验证了所提方法在降低能耗与保障安全导航方面的有效性。