Biological evidence shows that animals are capable of evading eminent collision without using depth information, relying solely on looming stimuli. In robotics, collision avoidance among uncooperative vehicles requires measurement of relative distance to the obstacle. Small, low-cost mobile robots and UAVs might be unable to carry distance measuring sensors, like LIDARS and depth cameras. We propose a control framework suitable for a unicycle-like vehicle moving in a 2D plane that achieves collision avoidance. The control strategy is inspired by the reaction of invertebrates to approaching obstacles, relying exclusively on line-of-sight (LOS) angle, LOS angle rate, and time-to-collision as feedback. Those quantities can readily be estimated from a monocular camera vision system onboard a mobile robot. The proposed avoidance law commands the heading angle to circumvent a moving obstacle with unknown position, while the velocity controller is left as a degree of freedom to accomplish other mission objectives. Theoretical guarantees are provided to show that minimum separation between the vehicle and the obstacle is attained regardless of the exogenous tracking controller.
翻译:生物证据表明,动物能够在不使用深度信息的情况下躲避显著的碰撞,而完全依靠逼近的刺激因素。在机器人中,不合作的车辆避免碰撞要求测量距离障碍的相对距离。小型、低成本的移动机器人和无人驾驶飞行器可能无法携带测距传感器,如LIDARS和深度摄像头。我们提议一个适合在2D平面上移动的类似单周期的车辆的管制框架,以达到避免碰撞的目标。控制战略的灵感来自无脊椎动物对接近障碍物的反应,完全依靠视线角度、LOS角度率和时间到轨道作为反馈。这些数量可以很容易地从移动机器人上的单镜视系统估算。拟议的避险法要求绕绕绕位置不明的移动障碍物,而速度控制器则留作实现其他任务目标的自由度。提供了理论保证,以表明无论外源跟踪控制器如何,车辆与障碍之间的最小分离是达到的。