Learning to plan people-aware trajectories for robot navigation: A genetic algorithm*

Nowadays, one of the emergent challenges in mobile robotics consists of navigating safely and efficiently in dynamic environments populated by people. This paper focuses on the robot’s motion planning by proposing a learning-based method to adjust the robot’s trajectories to people’s movements by respecting the proxemics rules. With this purpose, we design a genetic algorithm to train the navigation stack of ROS during the goal-based navigation while the robot is disturbed by people. We also present a simulation environment based on Gazebo that extends the animated model for emulating a more natural human’s walking. Preliminary results show that our approach is able to plan people-aware robot’s trajectories respecting proxemics limits without worsening the performance in navigation.