Negotiating Visibility for Safe Autonomous Navigation in Occluding and Uncertain Environments
Negotiating Visibility for Safe Autonomous Navigation in Occluding and Uncertain Environments
复制标题
DOI：
10.1109/lra.2021.3068701
复制
发表时间：
2021-07-01
影响因子：
5.2
通讯作者：
Bezzo, Nicola
中科院分区：
文献类型：
--
作者：
Higgins, Jacob;Bezzo, Nicola
对于自主移动机器人（AMR）来说，在闭塞的环境中导航是一项具有挑战性的任务，因为它们必须平衡安全和速度，以便在不确定的环境中流畅地绕过闭塞。这是因为现实世界的环境中有动态的参与者，这些参与者可能会在机器人运动期间被遮挡，从而引入不确定性。消除这种不确定性的一个关键因素是以这种方式移动，以最大限度地提高对这些闭塞的感知。这封信提出了一种新的控制框架，结合了感知和安全约束，导致在存在闭塞时快速安全的运动。感知是通过基于模型预测控制（MPC）的方法来满足的，该方法提供的输入增加了闭塞周围的能见度，同时通过将不确定性建模为根据当前观察和预期交通运动得出的预计占用概率来提高安全性。在能见度、安全性和速度方面的改进在模拟中得到了展示，并在无人驾驶地面车辆上进行了实验验证。
Navigation through an occluded environment is a challenging task for autonomous mobile robots (AMR), since they must balance both safety and speed in an attempt to fluidly steer around occlusions in uncertain environments. This is because real world environments have dynamic actors that may be occluded to the robot during motion, introducing uncertainty. One key element of eliminating this uncertainty is moving in such a way to maximize perception around these occlusions. This letter presents a novel control framework that combines both perception and safety constraints, resulting in motion that is quick and safe when occlusions are present. Perception is satisfied using a model predictive control (MPC)-based approach to provide inputs that increase visibility around occlusions while safety is promoted by modeling uncertainties as projected probabilities of occupancy derived from current observation and expected traffic motion. Improvements in visibility, safety, and speed are shown in simulations and are experimentally validated using an unmanned ground vehicle.