5 accepted papers
Multi-target encirclement with unmanned aerial vehicle (UAV) swarms is critical for military and civilian applications such as surveillance and disaster response. Existing methods face limitations in adaptability, primarily due to their reliance on predefined formations, excessive communication requ
Deep reinforcement learning (DRL) has recently emerged as a promising tool for tackling pursuit-evasion tasks. However, most existing DRL-based pursuit approaches still rely on individual rewards and struggle with complex scenarios. To address these challenges, we propose a knowledge-enhanced DRL ap
Image based 3 Degrees-of-Freedom (DoF) cross-view geo-localization aims to estimate the position and orientation of a camera on the ground by matching the captured ground image with geo-tagged aerial images. However, most existing methods do not sufficiently exploit the difference between positive a…
A swarm of fixed-wing unmanned aerial vehicles (UAVs) is expected to efficiently accomplish various tasks in complex scenarios. This paper proposes an attention-based population-invariant multi-agent deep reinforcement learning (MADRL) approach to deal with the decentralized collision-free flocking…
Developing the flocking behavior for a dynamic squad of fixed-wing UAVs is still a challenge due to kinematic complexity and environmental uncertainty. In this paper, we deal with the decentralized flocking and collision avoidance problem through deep reinforcement learning (DRL). Specifically, we f…