Gene

Robot

Materials science

Robustness (evolution)

Real-time computing

Archaeology

Obstacle avoidance

Telecommunications

Biochemistry

Computer science

Obstacle

Geography

Global Positioning System

Mobile robot

Artificial intelligence

Flocking (texture)

Remote sensing

Composite material

Chemistry

Lidar

Computer vision

IEEE Transactions on Industrial Electronics

This paper presents a sophisticated vision-aided flocking system for unmanned aerial vehicles (UAVs), which is able to operate in GPS-denied unknown environments for exploring and searching missions, and also able to adopt two types of vision sensors, day and thermal cameras, to measure relative motion between UAVs in different lighting conditions without using wireless communication. In order to realize robust vision-aided flocking, an integrated framework of tracking-learning-detection on the basis of multifeature coded correlation filter has been developed. To achieve long-term tracking, a redetector is trained online to adaptively reinitialize target for global sensing. An advanced flocking strategy is developed to address the autonomous multi-UAVs' cooperative flight. Light detection and ranging (LiDAR)-based navigation modules are developed for autonomous localization, mapping, and obstacle avoidance. Flight experiments of a team of UAVs have been conducted to verify the performance of this flocking system in a GPS-denied environment. The extensive experiments validate the robustness of the proposed vision algorithms in challenging scenarios.

Vision-Aided Multi-UAV Autonomous Flocking in GPS-Denied Environment