Vehicle dynamics is typically handled by models whose parameters are found through system identi?cation or manually computed from the vehicles characteristics. While these methods provide accurate theoretical dynamical models, they may not take into account differences between individual vehicles, lack adaptability to new environments and may not handle sophisticated models, requiring hand-crafted heuristics for backwards motion for example. Similarly to space and aerial maneuver-based planning methods, we demonstrate a simple and computationally fast planning method for ground robots with obstacle avoidance. It bypasses the need for model parameters identi?cation and hand-crafted heuristics, learns the particularities of individual vehicles, allows on-line adaptation and sophisticated models. Human-driven or autonomously driven trajectories are recorded and stored into a trajectory bank. While in learning mode, the robot records each traveled trajectory and places it into a bank, indexed by the initial speeds of each left and right wheels and the ending position at a ?xed radius. Only the best trajectories are stored in the trajectory bank and then reused during autonomous runs for optimal short-range planning. Pre-computed (but not recorded) trajectories have been used in previous work and provide an important computational advantage over on-line computation methods, which are less practical in real-time applications due to the high-dimensional search space. A collision-free platform was developed without any hand-crafted heuristics or knowledge about the vehicles characteristics. This method is demonstrated on the LAGR platform, a non-holonomic (differential drive) off-road mobile robot.


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    Title :

    Learning maneuver dictionaries for ground robot planning


    Contributors:


    Publication date :

    2008


    Size :

    6 Seiten, 9 Bilder, 20 Quellen


    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English




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