End-to-End Motion Planning based on Multi-Task Learning for Mobile Robots

Abstract

In order for mobile robots to move autonomously, collision avoidance is an essential capability. Thus far, end-to-end motion planners based on Deep Neural Network and Convolutional Neural Network have been proposed. However, robots based on these planners through imitation learning sometimes fail to avoid obstacles in unknown environments. This is due to generalization performance of the planners. In order to improve the generalization performance, we propose a novel motion planner based on DNN and CNN using multi-task learning. Through the experiments, we show the effectiveness of the proposed motion planner for collision avoidance by comparing the generalization performance of DNN and CNN.