For an autonomous vehicle that travels off-road, the driving speed is limited by the driving circumstances. To decide on a stable manoeuvring speed, the driving system should consider road conditions such as the height of an obstacle and road roughness. In general, an autonomous vehicle has many sensors to preview road conditions, and the information gathered by these sensors can be used to find the proper path for the vehicle to avoid unavoidable obstacles. However, sensor data are insufficient for determining the optimal vehicle speed, which could be obtained from the dynamic response of the vehicle. This paper suggests an algorithm that can determine the optimal vehicle speed running over irregular rough terrains such as when travelling off-road. In the determination of the manoeuvring speed, the vehicle dynamic simulation is employed to decide whether the vehicle response is within or beyond the prescribed limits. To determine the manoeuvring speed in real time, the dynamic simulation should be finished much more quickly than the real motion speed of the vehicle. In this paper, the equation of motion of the vehicle is derived in terms of the chassis local coordinates to reduce the simulation time. The velocity transformation technique, which combines the generality of Cartesian coordinates and the efficiency of relative coordinates, was combined with a symbolic computation to enhance further the computational efficiency. First the developed algorithm calculates the level of the previewed road roughness to determine the manoeuvring speed. Then, the maximum stable speed is judged against the database, which already has stored the maximum vertical accelerations as a function of the road roughness and vehicle speed.


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    Title :

    Manoeuvring speed of a 6×6 autonomous vehicle using a database obtained from multi-body dynamic simulation


    Contributors:
    Cho, S-L (author) / Yi, K-C (author) / Lee, J-H (author) / Yoo, W-S (author)


    Publication date :

    2009-08-01


    Size :

    7 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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