Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy. In practice, however, tracking error can be affected by many factors, and could lead to potential collisions when the robot must traverse a cluttered environment. To address this problem, this paper proposes a novel receding-horizon motion planning approach based on Model Predictive Path Integral (MPPI) control theory -- a flexible sampling-based control technique that requires minimal assumptions on vehicle dynamics and cost functions. This flexibility is leveraged to propose a motion planning framework that also considers a data-informed risk function. Using the MPPI algorithm as a motion planner also reduces the number of samples required by the algorithm, relaxing the hardware requirements for implementation. The proposed approach is validated through trajectory generation for a quadrotor unmanned aerial vehicle (UAV), where fast motion increases trajectory tracking error and can lead to collisions with nearby obstacles. Simulations and hardware experiments demonstrate that the MPPI motion planner proactively adapts to the obstacles that the UAV must negotiate, slowing down when near obstacles and moving quickly when away from obstacles, resulting in a complete reduction of collisions while still producing lively motion.

翻译：当前自主移动机器人的运动规划方法通常假设系统的底层控制器能够以极高的精度跟踪规划的运动。然而在实际应用中，跟踪误差可能受多种因素影响，当机器人需要穿越密集环境时，这种误差可能导致潜在的碰撞。针对此问题，本文提出了一种基于模型预测路径积分（MPPI）控制理论的新型滚动时域运动规划方法——该方法是一种灵活的基于采样的控制技术，对车辆动力学和代价函数的要求最小。利用这种灵活性，本文提出了一种同时考虑数据驱动风险函数的运动规划框架。将MPPI算法作为运动规划器使用，还能减少算法所需的样本数量，从而降低对硬件实现的要求。所提方法通过四旋翼无人机的轨迹生成进行了验证，其中快速运动会增大轨迹跟踪误差，并可能导致与附近障碍物碰撞。仿真与硬件实验表明，MPPI运动规划器能主动适应无人机需绕行的障碍物，在靠近障碍物时减速、远离障碍物时加速，从而在保持生动运动的同时完全消除碰撞。