Improving Decision-Making-Process for Robot Navigation Under Uncertainty

Abstract

Designing an autonomous system is a challenging task nowadays, and this is mainly due to two challenges such as conceiving a reliable system in terms of decisions accuracy (performance) and guaranteeing the robustness of the system to noisy inputs. A system is called efficient, if it is simultaneously reliable and robust. In this paper, we consider robot navigation under uncertain environments in which robot sensors may generate disturbed measures affecting the robot decisions. We aim to propose an efficient decision-making model, based on Deep Neural Network (DNN), for robot navigation. Hence, we propose an adversarial training step based on data augmentation to improve robot decisions under uncertain environment. Our contribution is based on investigating data augmentation which is based on uncertainty noise to improve the robustness and performance of the decision model. We also focus on two metrics, Efficiency and Pareto Front, combining robustness and performance to select the best data augmentation rate. In the experiment stage, our approach is validated on a public robotic data-set.