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

TL;DR

This paper addresses the challenge of efficiently controlling stationary sensors to track multiple moving objects, providing theoretical limitations and a practical algorithm that achieves near-optimal energy use in real-time scenarios.

Contribution

It introduces a geometric algorithm for minimizing peak power consumption in tracking multiple moving objects, along with theoretical results on the problem's computational hardness.

Findings

The algorithm finds optimal solutions for the min-max power problem.

Runtimes are seconds for large instances, enabling real-time application.

Theoretical results show no polynomial-time algorithm can guarantee optimal coverage.

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.