HMD Vision-based Teleoperating UGV and UAV for Hostile Environment using   Deep Learning
  The necessity of maintaining a robust antiterrorist task force has become imperative in recent times with resurgence of rogue element in the society. A well equipped combat force warrants the safety and security of citizens and the integrity of the sovereign state. In this paper we propose a novel teleoperating robot which can play a major role in combat, rescue and reconnaissance missions by substantially reducing loss of human soldiers in such hostile environments. The proposed robotic solution consists of an unmanned ground vehicle equipped with an IP camera visual system broadcasting real-time video data to a remote cloud server. With the advancement in machine learning algorithms in the field of computer vision, we incorporate state of the art deep convolutional neural networks to identify and predict individuals with malevolent intent. The classification is performed on every frame of the video stream by the trained network in the cloud server. The predicted output of the network is overlaid on the video stream with specific colour marks and prediction percentage. Finally the data is resized into half-side by side format and streamed to the head mount display worn by the human controller which facilitates first person view of the scenario. The ground vehicle is also coupled with an unmanned aerial vehicle for aerial surveillance. The proposed scheme is an assistive system and the final decision evidently lies with the human handler. 
Abhishek Sawarkar, Vishal Chaudhari, Rahul Chavan, Varun Zope, Akshay   Budale, Faruk Kazi
cs.RO