Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Learning for Autonomous Navigation


    Contributors:

    Conference:

    Neural Information Processing Systems (NIPS) Workshop Machine Learning Based Robotics in Unstructured Environments ; 2005 ; British Columbia, Canada


    Publication date :

    2005-12-09


    Type of media :

    Preprint


    Type of material :

    No indication


    Language :

    English




    Learning for autonomous navigation

    Angelova, Anelia / Howard, Andrew / Matthies, Larry et al. | NTRS | 2005


    Autonomous navigation through case-based learning

    Weng, J.J. / Shaoyun Chen | IEEE | 1995


    Autonomous Navigation Through Case-Based Learning

    Weng, J. J. / Chen, S. / IEEE; Computer Society; Technical Committee for Pattern Analysis and Machine Intelligence | British Library Conference Proceedings | 1995


    Autonomous UAV Navigation Using Reinforcement Learning

    Pham, Huy X. / La, Hung M. / Feil-Seifer, David et al. | ArXiv | 2018

    Free access