Geometry helps drones handoff tracked targets accurately in flight
PATH: Continuous Target Sensing among Autonomous Cooperative Drones
Robotics
Summary
Drones often need to keep watching a target for a long time, but they run out of battery fast. To solve this, the authors made a way for one drone to pass the tracking job to another smoothly. Their method uses 3D shape data and special markers to help the receiving drone find the exact target even if it looks different from another angle. Tests showed the method is very accurate and works well even when visuals are unclear.
What this means in practice
- •For drone operators: Enable multiple drones to reliably share target tracking to extend flight missions without losing the target.
- •For security surveillance teams: Use cooperating drones to continuously monitor moving targets in complex environments with improved accuracy despite visual challenges.
Authors
Heegyeong Kim, Alice James, Avishkar Seth, Endrowednes Kuantama, Jane Williamson, Yimeng Feng, Richard Han
Abstract
Continuous target sensing by uncrewed aerial vehicles (UAVs) is constrained by limited flight endurance, motivating the transfer of tracking responsibility between cooperating UAVs. Such a handoff requires the receiver to identify the same physical target currently tracked by the sender despite differences in viewpoint, scale, and target appearance. Existing approaches based on global target localization or appearance-based cross-view association are limited by positioning uncertainty or ambiguous visual features. This paper presents Perspective Alignment \& Tracking Handoff (\textbf{PATH}), a platform-agnostic, geometry-assisted sensing and verification framework for target handoff between two moving UAVs. The sender reconstructs the tracked target as a metric 3D point using RGB-D sensing, while the receiver estimates its relative pose from a fiducial observation and projects the transmitted target point into its own image as a spatial prior for target acquisition. The receiver-generated candidate is then returned to the sender and verified through a cross-view Mutual Agreement Handshake before tracking responsibility is transferred. Real-world UAV experiments show mean relative-position and target-position errors of 0.047~m and 0.030~m, respectively. Under visually ambiguous conditions, PATH achieves 96.0\% frame-level receiver-side target acquisition accuracy, with 2.0\% false-positive and 2.0\% false-negative rates. A sensor-error sensitivity analysis shows that relative-pose uncertainty is the dominant contributor to receiver-view projection error. The implementation operates at video rate with compact inter-UAV communication below 16~kB/s at 60~Hz, demonstrating the feasibility of lightweight geometry-assisted target handoff on resource-constrained UAV platforms.