Safe and natural navigation in dynamic environments, learned from human behavior

Safe and natural navigation in dynamic environments, learned from human behavior

Croll, Ewoud (TU Delft Mechanical, Maritime and Materials Engineering)

Alonso Mora, J. (mentor)
Ferreira de Brito, B.F. (graduation committee)
de Winter, J.C.F. (graduation committee)

Delft University of Technology

2020-05-08

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

Autonomous
Robot
Social
Navigation
Motion Planning
Neural Network
Deep Learning
Interaction
Model Predictive Control

http://resolver.tudelft.nl/uuid:3d50c5e1-1727-4999-978f-ff0b4a41c072

Student theses

master thesis

© 2020 Ewoud Croll