Cooperative Multi-AAV Path Planning for Discovering and Tracking Multiple Radio-Tagged Targets
Yukang Cui, Jiabin Chen, Hong Sheng Lin, Zhan Shu, Tingwen Huang · IEEE Transactions on Systems Man and Cybernetics Systems · 2025
Discovering and tracking wildlife targets are essential for gaining insights into the behavioral patterns and habits of animals within their natural habitats. With low cost and high maneuverability, mini autonomous aerial vehicles (AAVs) can achieve robust and rapid locating and tracking of multiple targets through collaboration. This work proposes a method for multitarget task allocation and path planning for AAV swarms, addressing the challenges of locating and tracking multiple wildlife with very high frequency (VHF) radio tags while avoiding potential disturbances to the wildlife. Our approach proposes a layered framework for the multi-AAV multitarget wildlife tracking problem: 1) the state estimation layer performs fast receiver signal strength indicator (RSSI) signal acquisition and employs the particle filtering algorithm to localize targets’ positions; 2) the task assignment layer uses a quadratic allocation method for AAVs’ real-time target allocation, starting with reasonable initial target sets via mixed-integer programming and efficiently readjusting targets based on real-time environment; and 3) the motion planning layer introduces an optimization-based approach to generate smooth and executable trajectories that can simultaneously ensure desired safe distances from objects of interest. Simulation experiments validate the effectiveness of the obtained AAV swarm tracking scheme.