| Abstract |
Autonomous navigation systems on unmanned aerial vehicles (UAVs) equipped with multiple sensors are essential to various applications in the smart city and intelligent transportation. However, the general autonomous navigation models are markedly influenced by the prior knowledge from training environments, which in turn are not applicable in unknown environments. To address this issue, we propose an online autonomous UAV navigation system named as multi-sensor data-fusion-based autonomous navigation (MDFAN) system for unknown flight environments, including the collision avoidance and path planning. Specifically, first, the newly MDFAN system formulates the navigation problem as a decision-making path planning problem to reduce the dependence of prior knowledge of the flight environment. Secondly, we develop a multi-sensor data-fusion-based method to extract more effective local environment information for mining the inherent inter-relationship between the local environment information and the current state of the UAV. Thirdly, we propose a deep reinforcement learning method for handling uncertain situations of the unknown environment. Finally, we validated our method both on the simulated and real-world environments. © 2019 IEEE. |
