Safe and natural navigation in dynamic environments, learned from human behavior
E.T. Croll (TU Delft - Mechanical Engineering)
Javier Alonso-Mora – Mentor (TU Delft - Learning & Autonomous Control)
Bruno Ferreira Ferreira de Brito – Graduation committee member (TU Delft - Learning & Autonomous Control)
JCF de Winter – Graduation committee member (TU Delft - Human-Robot Interaction)
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Abstract
Social Navigation is the task of robot motion planning in an environment shared with humans.This is an especially hard sub-problem of motion planning because the planner has to dealwith a dynamic, continuous and unpredictable environment. We present a local motionplanner, namely Neural Network Model Predictive Control, for autonomous ground vehiclesin highly dynamic environments. A neural network is trained to plan local trajectories basedon human behavior data. It has therefor learned to mimic how a person would behave in sucha situation. The trajectory plan of the neural network is used as guidance and initializationof a model predictive controller. This MPC creates a kinematically feasible trajectory andassures collision avoidance with the static and dynamic obstacles in the environment withinits receding horizon. This combined planner and controller is tested in simulation and showedon a real autonomous robot