A Hybrid Human-in-the-Loop Deep Reinforcement Learning Method for UAV Motion Planning for Long Trajectories with Unpredictable Obstacles

Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things (IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to-reach areas. Ensuring collision-free navigation for these UAVs is crucial in achieving this goal. However, existing UAV collision-avoidance methods face two challenges: conventional path-planning methods are energy-intensive and computationally demanding, while deep reinforcement learning (DRL)-based motion-planning methods are prone to make UAVs trapped in complex environments-especially for long trajectories with unpredictable obstacles-due to UAVs' limited sensing ability. To address these challenges, we propose a hybrid collision-avoidance method for the real-time navigation of UAVs in complex environments with unpredictable obstacles. We firstly develop a Human-in-the-Loop DRL (HL-DRL) training module for mapless obstacle avoidance and secondly establish a global-planning module that generates a few points as waypoint guidance. Moreover, a novel goal-updating algorithm is proposed to integrate the HL-DRL training module with the global-planning module by adaptively determining the to-be-reached waypoint. The proposed method is evaluated in different simulated environments. Results demonstrate that our approach can rapidly adapt to changes in environments with short replanning time and prevent the UAV from getting stuck in maze-like environments.

Automated summary

Unmanned Aerial Vehicles (UAVs) are crucial for collecting and transmitting data from remote areas, and collision-free navigation is essential for their successful operation. Existing methods for UAV collision avoidance face challenges such as high energy consumption and limited sensing ability. To address these challenges, we propose a hybrid collision-avoidance method that combines human-in-the-loop deep reinforcement learning (HL-DRL) and global planning. This method has been evaluated in simulated environments and has shown rapid adaptation and the ability to prevent UAVs from getting stuck in complex environments.