EAGER: Uncertainty-aware Planning for Robot Navigation in Human Environments

从服务机器人到自动驾驶汽车，机器人越来越多地融入社会。 然而，为了既安全又高效，他们需要更好地了解人们如何在非结构化环境中导航，并考虑人们自己的路径来规划路径。 在这个项目中开发的规划方法将产生机器人路径，满足最大限度地减少机器人与人碰撞的风险，同时最大限度地减少其预期的任务完成时间的双重目标。这项工作的独特之处在于，机器人动态地估计可预测的行人在任何时间点的行为，并相应地调整其计划，在不可预测的情况下自动采取更安全，更保守的路径，并在情况更可预测时强调效率。 机器人在人类环境中自主规划和执行安全、有效路径的能力将提高它们与人类协作的能力，特别是在拥挤的情况下，如工厂、学校、医院、或商场。开发的导航方法遵循一个感知-计划-预测-行动循环，其中机器人计划通过找到中到避免与可能的障碍状态的预期未来分布碰撞的长期轨迹。这项工作的目标分为两个任务：第一个改进现有的行人路径建模技术，通过增加预测的不确定性估计。 这是通过迭代最大似然框架来实现的。第二个任务找到机器人轨迹，最大限度地减少路径成本函数，占控制和碰撞成本，同时尊重机器人动力学。这是使用迭代的、基于梯度的轨迹优化方法来完成的。 由此产生的导航方法进行评估，在模拟和真实的行人数据。

Robots are increasingly being integrated into society, from service robots to autonomous cars. To be both safe and efficient, however, they need to better understand how people navigate in unstructured environments and plan paths that take people's own paths into consideration. The planning methods developed in this project will generate robot paths that satisfy the dual goals of minimizing the robot's risks of collision with people while simultaneously minimizing its expected task completion time. Unique to this work, the robot dynamically estimates how predictable pedestrians are behaving at any point in time and adapts its plans accordingly, automatically taking safer, more conservative, paths when in less predictable situations, and emphasizing efficiency when the situation is more predictable. The ability for robots to autonomously plan and execute safe, efficient paths in human environments will improve their ability to operate in collaboration with humans, especially in crowded situations, such as in factories, schools, hospitals, or malls.The navigation method developed follows a sense-plan-predict-act loop where robot plans are computed by finding medium-to-long term trajectories that avoid collision with an expected future distribution of likely obstacle states. The goals of this work split into two tasks: The first improves existing pedestrian path-modeling techniques by augmenting the predictions with estimates of uncertainty. This is accomplished through an iterative maximum-likelihood framework. The second task finds robot trajectories that minimize a path-cost function, accounting for both control and collision costs, while respecting robot dynamics. This is done using an iterative, gradient-based trajectory optimization approach. The resulting navigation approach is evaluated in both simulation and on real pedestrian data.