Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done with as few efforts as possible. Given the limited time and the wide range of problematic to be tackled, absolute navigation performances while important was not the main objective. The emphasis was rather put on studying the sim2real gap which is one the major bottlenecks of modern robotics and autonomous navigation. To design the navigation pipeline four main challenges arise; environment, localization, navigation and planning. The iGibson simulator is picked for its photo-realistic textures and physics engine. A topological approach to tackle space representation was picked over metric approaches because they generalize better to new environments and are less sensitive to change of conditions. The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module. These modules utilize three different networks, an image representation extractor, a passage detector and a local policy. The laters are trained on specifically tailored tasks with some associated datasets created for those specific tasks. Localization is the ability for the agent to localize itself against a specific space representation. It must be reliable, repeatable and robust to a wide variety of transformations. Localization is tackled as an image retrieval task using a deep neural network trained on an auxiliary task as a feature descriptor extractor. The local policy is trained with behavioral cloning from expert trajectories gathered with ROS navigation stack.
翻译:自主导航意味着代理人能够在没有人类干预或监督的情况下导航,它影响到高层规划和低水平控制。导航处于多个学科的交叉路口,它把计算机视觉、机器人和控制的概念结合起来。这项工作的目的是在模拟中建立一个导航管道,通过尽可能少的努力可以将其转移到真实世界。鉴于时间有限,需要处理的问题范围很广,绝对导航性操作虽然不是主要目标。重点更是研究模拟现实差距,这是现代机器人和自主导航的主要瓶颈之一。为了设计导航管道,出现了四种主要挑战;环境、本地化、导航和规划。iGibson模拟器是为其光真知灼见的文本和物理引擎所选取的。处理空间代表的表层学方法被选为衡量方法,因为它们对新的环境比较更好,对条件的变化不那么敏感。导航管道被拆分解为本地化模块、规划模块和本地导航模块。这些模块利用三种不同的网络,一个经过训练的图像代表器,一个经过精细化的移动系统,一个经过专门训练的移动工具,一个特定的移动工具,一个经过本地化任务必须用来收集。