Human-Robot Teaming and Coordination in Day and Night Environments

As robots are sharing work spaces with human, human-robot teamwork is becoming increasingly important. It is foreseeable that the daily work team will be composed of human and robots. The integration of the appropriate decision-making process is an essential part to design and develop the team. If robots can understand the activities and intents of human, it is convenient for a person to cooperate with robots in a natural manner. This paper proposes a system that enables robots to understand human pose and execute given command. The system provides two options for different hardware systems: the first one is suitable for powerful computational units; the second model is compact and efficient on a normal robot platform. In order to enrich application scenarios, we propose a method to extract human pose from thermal images so that our system can be used in all-weather scenario. In addition, we collected extensive training data and trained a MLP neural network to classify several human poses. The experimental results show the accuracy and efficiency of the proposed MLP neural network in day and night environments.