Search

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (BSs) to provide auxiliary communication services. In this letter, we propose a deep reinforcement learning (DRL)‐based dynamic deployment method for multi‐UAV communications. The phasic policy gradient (PPG) is designed to improve the sample efficiency and the attention of the multi‐UAV deployment. Simulation results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A path planning approach is proposed that is in accordance with Collision Regulations (COLREGS) for avoidance of a potential collision and considers the time-varying trajectory uncertainties of an autonomous surface vehicle (ASV) and an obstacle. Unfortunately, as the motion information for each object includes various uncertainties caused by characteristics of the navigation and perception systems, the trajectory uncertainties increase linearly during the prespecified time in a trajectory prediction process. This trajectory uncertainty is preferentially modelled using the error covariance of a tracking filter designed for estimating motion information. Subsequently, collision risk is computed using a probabilistic method, and a collision risk zone (CRZ) is configured on the predicted trajectory. Finally, a COLREGS-compliant path is planned with consideration of the dynamic characteristics of the ASV to avoid the CRZ. Simulation results that demonstrate the feasibility of the proposed approach with marine traffic examples are presented and discussed. [ABSTRACT FROM AUTHOR]