SmartUAV: A Novel Smart Unmanned Aerial Vehicle with Emerging Artificial Intelligence on Chip

Due to the complexity of tactical mission as well as the uncertainty of the practical environment, more and more Unmanned Aerial Vehicles (UAVs) have been seeking innovative and reliable sensing,  navigation, path planning, and real-time control technique that can be used even in harsh environment such as the GPS-denied environment. With the rapid development of machine learning, an emerging artificial intelligence (AI) on the chip can be a promising technique to fully enable the autonomous UAVs swarming capability in practical even under harsh environments. In this project, we aim to develop and verify a new type of Smart Unmanned Aerial Vehicle (Smart UAV) with emerging artificial intelligence on-chip that possesses four prominent properties, i.e. scalability, adaptability, resiliency, and autonomy, and can be used for various tactical environments. The novel Hierarchical Hybrid Artificial Intelligence (H2AI) framework that has designed a set of appropriate AI techniques and implemented them into different Smart UAV Layers, i.e. sensing/perception layer, path planning layer, and flight control layer. The Hybrid Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) based Online Dead Reckoning Navigation for Smart UAV will significantly upgrade the dead reckoning navigation accuracy; while the designed multiscale switching reinforcement learning based UAV path planning can enable the complex UAV mission, e.g. swarming under the GPS-denied harsh environment.