Navigation is the one of the most fundamental capabilities required for mobile robots, allowing them to traverse from a source to a destination. Conventional approaches rely heavily on the existence of a predefined map which is costly both in time and labour to acquire. In addition, maps are only accurate at the time of acquisition and due to environmental changes degrade over time. We argue that this strict requirement of having access to a high-quality map fundamentally limits the realisability of robotic systems in our dynamic world. In this thesis, we investigate how to develop practical robotic navigation, motivated by the paradigm of mapless navigation and inspired by recent developments in Deep Reinforcement Learning (DRL). One of the major issues for DRL is the requirement of a diverse experimental setup with millions of repeated trials. This clearly is not feasible to acquire from a real robot through trial and error, so instead we learn from a simulated environment. This leads to the first fundamental problem which is that of bridging the reality gap from simulated to real environments, tackled in Chapter 3. We focus on the particular challenge of monocular visual obstacle avoidance as a low-level navigation primitive. We develop a DRL approach that is trained within a simulated world yet can generalise well to the real world. Another issue which limits the adoption of DRL techniques for mobile robotics in the real world is the high variance of the trained policies. This leads to poor convergence and low overall reward, due to the complex and high dimensional search space. In Chapter 4, we leverage simple classical controllers to provide guidance to the task of local navigation with DRL, avoiding purely random initial exploration. We demonstrate that this novel accelerated approach greatly reduces sample variance and significantly increases achievable average reward. The last challenge we consider is that of sparse visual guidance for mapless navigation. In Chapter 5, we present an innovative approach ...


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    Title :

    Reinforcement learning based mapless robot navigation


    Contributors:

    Publication date :

    2020-04-17


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629




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