This paper present a trajectory planning algorithm for a robot operating in dynamic human environments. Environments such as pedestrian streets, hospital corridors and train stations. We formulate the problem as planning a minimal cost trajectory through a potential field, defined from the perceived position and motion of persons in the environment. A Rapidly-exploring Random Tree (RRT) algorithm is proposed as a solution to the planning problem. A new method for selecting the best trajectory in the RRT, according to the cost of traversing a potential field, is presented. The RRT expansion is enhanced to direct the search and account for the kinodynamic robot constraints. Compared to standard RRT, the algorithm proposed here find the robot control input that will drive the robot towards a new sampled point in the configuration space. The effect of the input is simulated, to add a reachable vertex to the tree. Instead of executing a whole trajectory, when planned, the algorithm uses an Model Predictive Control (MPC) approach, where only a short segment of the trajectory is executed while a new iteration of the RRT is done. The planning algorithm is demonstrated in a simulated pedestrian street environment.


    Access

    Download


    Export, share and cite



    Title :

    Trajectory Planning for Robots in Dynamic Human Environments


    Contributors:

    Publication date :

    2010-01-01


    Remarks:

    Svenstrup , M , Bak , T & Andersen , H J 2010 , Trajectory Planning for Robots in Dynamic Human Environments . in Trajectory Planning for Robots in Dynamic Human Environments : Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010) . IEEE Press , I E E E International Conference on Intelligent Robots and Systems. Proceedings , pp. 4293-4298 . https://doi.org/10.1109/IROS.2010.5651531



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629