Autonomous driving technology is continuously evolving at an accelerated pace. The road environment is always uncertain, which requires an evasive manoeuvre that an autonomous vehicle can take. This evasive behaviour to avoid accidents in a critical situation is analogous to autonomous racing that operates at the limits of stable vehicle handling. In autonomous racing, the vehicle must operate in highly nonlinear operating conditions such as high-speed manoeuvre on sharp turns, avoiding obstacles and slippery road conditions. These dynamically changing racing situations require advanced path planning systems with obstacle avoidance executed in real-time. Therefore, the motion planning problem for autonomous racing is analogous to safe and reliable autonomous vehicle operation in critical situations. This thesis project evaluates the application of traction adaptive motion planning to autonomous racing on different road surfaces for a small-scale test vehicle in real-time. The evaluation is based on a state-of-the-art algorithm that uses a combination of optimization, trajectory rollout, and constraint adaption framework called "Sampling Augmented Real-Time Iteration (SAARTI)". SAARTI allows motion planning and control with respect to time-varying vehicle actuation capabilities while taking locally adaptive traction into account for different parts of the track as a constraint. Initially, the SAARTI framework is adapted to work with the SmallVehicles-for-Autonomy (SVEA) system; then, the whole system is simulated in a ROS (Robot Operating System) based SVEA simulator with a Hardware-in-the-loop setup. Later, the same setup is used for the real time experiments that are carried out using the SVEA vehicles, and the different critical scenarios are tested on the SVEA vehicle. The emphasis was given to the experimental results; therefore, the results also consider computationally intensive localization inputs while the motion planner was implemented in real-time instead of a simulation setup. The experimental results ...


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    Title :

    Traction Adaptive Motion Planning for Autonomous Racing ; Tractionadaptiv rörelseplanering för autonom racing


    Contributors:

    Publication date :

    2022-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629