To avoid getting stuck or causing damage to a vehicle or its surroundings a driver must be able to identify obstacles and adapt speed to ground conditions. An automatically controlled vehicle must be able to handle these identifications and adjustments by itself using sensors, actuators and control software. By storing properties of the surroundings in a map, a vehicle revisiting an area can benefit from prior information. Rough ground may cause oscillations in the vehicle chassis. These can be measured by on-board motion sensors. For obstacle detection, a representation of the geometry of the surroundings can be created using range sensors. Information on where it is suitable to drive, called traversability, can be generated based on these kinds of sensor measurements. In this work, real semi-autonomous mobile robots have been used to create traverasbility maps in both simulated and real outdoor environments. Seeking out problems through experiments and implementing algorithms in an attempt to solve them has been the core of the work. Finding large obstacles in the vicinity of a vehicle is seldom a problem; accurately identifying small near-ground obstacles is much more difficult, however. The work additionally includes both high-level path planning, where no obstacle details are considered, and more detailed planning for finding an obstacle free path. How prior maps can be matched and merged in preparation for path planning operations is also shown. To prevent collisions with unforeseen objects, up-to-date traversability information is used in local-area navigation and obstacle avoidance.


    Access

    Download


    Export, share and cite



    Title :

    Mobile Robot Traversability Mapping : For Outdoor Navigation


    Contributors:

    Publication date :

    2012-01-01


    Remarks:

    Local LIU-TEK-LIC-2012:49


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Traversability classification using unsupervised on-line visual learning for outdoor robot navigation

    Kim, Dongshin / Sun, Jie / Oh, Sang-Min et al. | Tema Archive | 2006


    Autonomous Robot Navigation Using Traversability Indices

    Seraji, H. / International Federation of Automatic Control | British Library Conference Proceedings | 2004



    Quantifying Traversability of Terrain for a Mobile Robot

    Howard, Ayanna / Seraji, Homayoun / Werger, Barry | NTRS | 2005


    EAT: Environment Agnostic Traversability for reactive navigation

    Saucedo, Mario A. V. / Patel, Akash / Kanellakis, Christoforos et al. | BASE | 2024

    Free access