We study the problem of target stabilization with robust obstacle avoidance in robots and vehicles that have access only to vision-based sensors for the purpose of realtime localization. This problem is particularly challenging due to the topological obstructions induced by the obstacle, which preclude the existence of smooth feedback controllers able to achieve simultaneous stabilization and robust obstacle avoidance. To overcome this issue, we develop a vision-based hybrid controller that switches between two different feedback laws depending on the current position of the vehicle using a hysteresis mechanism and a data-assisted supervisor. The main innovation of the paper is the incorporation of suitable perception maps into the hybrid controller. These maps can be learned from data obtained from cameras in the vehicles and trained via convolutional neural networks (CNN). Under suitable assumptions on this perception map, we establish theoretical guarantees for the trajectories of the vehicle in terms of convergence and obstacle avoidance. Moreover, the proposed vision-based hybrid controller is numerically tested under different scenarios, including noisy data, sensors with failures, and cameras with occlusions.
翻译:我们研究目标稳定化问题,在机器人和车辆中避免强烈障碍,因为机器人和车辆只能为实时定位目的获取基于视觉的传感器,这一问题特别具有挑战性,因为障碍造成的地形障碍阻碍了平稳反馈控制器的存在,无法同时实现稳定并避免障碍;为解决这一问题,我们开发了一个基于视觉的混合控制器,根据车辆目前的位置,利用歇斯底里机制和数据辅助监督器,在两种不同的反馈法之间交换。文件的主要创新是将适当的感知图纳入混合控制器。这些地图可以从从车辆摄像头获得的数据中学习,并通过革命神经网络(CNN)对其进行培训。根据关于这一感知图的适当假设,我们为车辆轨迹的趋同和障碍避免建立了理论保障。此外,拟议的基于视觉的混合控制器在不同的情景下进行数字测试,包括噪音数据、故障传感器和隐蔽摄像头。