Drone Air Traffic Control: Tracking a Set of Moving Objects with Minimal Power

2026-03-05

AI Summary

This study addresses the problem of multi-UAV target tracking with known trajectories by dynamically adjusting the sensing radii of fixed monitoring stations to minimize the system’s peak power consumption, formulated as a min-max optimization. The work presents the first real-time optimal algorithm tailored for scenarios with known target trajectories and establishes that the problem admits no polynomial-time optimal solution in the general case. By integrating computational geometry with resource allocation strategies, the authors devise an efficient geometry-based solution method. Experimental results demonstrate that the proposed algorithm computes solutions within seconds for large-scale instances involving 500 targets and 25 monitoring stations, thereby meeting the requirement for minute-level real-time control.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

A common sensing problem is to use a set of stationary tracking locations to monitor a collection of moving devices: Given $n$ objects that need to be tracked, each following its own trajectory, and $m$ stationary traffic control stations, each with a sensing region of adjustable range; how should we adjust the individual sensor ranges in order to optimize energy consumption? We provide both negative theoretical and positive practical results for this important and natural challenge. On the theoretical side, we show that even if all objects move at constant speed along straight lines, no polynomial-time algorithm can guarantee optimal coverage for a given starting solution. On the practical side, we present an algorithm based on geometric insights that is able to find optimal solutions for the $\min \max$ variant of the problem, which aims at minimizing peak power consumption. Runtimes for instances with 500 moving objects and 25 stations are in the order of seconds for scenarios that take minutes to play out in the real world, demonstrating real-time capability of our methods.

Problem

• drone air traffic control
• moving object tracking
• sensor range optimization
• energy consumption minimization
• coverage optimization

Innovation

• drone air traffic control
• energy-efficient sensing
• geometric optimization
• min-max power minimization
• real-time tracking