论文标题

通过学习感知图,基于数据辅助的基于视力的混合控制,以避免障碍

Data-Assisted Vision-Based Hybrid Control for Robust Stabilization with Obstacle Avoidance via Learning of Perception Maps

论文作者

Murillo-Gonzalez, Alejandro, Poveda, Jorge I.

论文摘要

我们研究了目标稳定的问题,并在机器人和车辆中避免了强大的障碍物,这些障碍物仅用于实时定位,这些机器人只能访问基于视觉的传感器。由于障碍物引起的拓扑障碍,该问题尤其具有挑战性,这排除了能够同时稳定和避免障碍的平稳反馈控制器的存在。为了克服这个问题,我们开发了一个基于视觉的混合控制器,该混合控制器可以使用磁滞机制和数据辅助主管在两个不同的反馈定律之间切换。本文的主要创新是将合适的感知图纳入混合控制器。这些地图可以从从车辆中的摄像机获得的数据中学到,并通过卷积神经网络(CNN)训练。在此感知图上​​的适当假设下,我们就汇聚和避免障碍物的轨迹建立了对车辆轨​​迹的理论保证。此外,在不同的情况下,对基于视觉的混合控制器进行了数值测试,包括嘈杂的数据,失败的传感器以及带有遮挡的相机。

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.

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