Motion planning under uncertainty is of significant importance for safety-critical systems such as autonomous vehicles. Such systems have to satisfy necessary constraints (e.g., collision avoidance) with potential uncertainties coming from either disturbed system dynamics or noisy sensor measurements. However, existing motion planning methods cannot efficiently find the robust optimal solutions under general nonlinear and non-convex settings. In this paper, we formulate such problem as chance-constrained Gaussian belief space planning and propose the constrained iterative Linear Quadratic Gaussian (CILQG) algorithm as a real-time solution. In this algorithm, we iteratively calculate a Gaussian approximation of the belief and transform the chance-constraints. We evaluate the effectiveness of our method in simulations of autonomous driving planning tasks with static and dynamic obstacles. Results show that CILQG can handle uncertainties more appropriately and has faster computation time than baseline methods.

翻译：在不确定情况下进行机动规划对自动车辆等安全关键系统非常重要,这些系统必须满足必要的限制(例如避免碰撞),而系统动力或噪音传感器测量可能带来不确定性;然而,现有的机动规划方法无法在一般的非线性和非节点设置下有效找到稳健的最佳解决办法;在本文件中,我们提出了机会有限的高斯信仰空间规划等问题,并提议将受限制的迭代线性高斯(CILQG)算法作为一种实时解决方案。在这个算法中,我们反复计算了信仰的高斯近似值,并改变了机会约束。我们用静态和动态障碍模拟自主驾驶规划任务的方法的有效性。结果显示,CILQG能够更适当地处理不确定性,比基线方法更快地计算时间。