Optimal Real Time Drone Path Planning for Harvesting Information from a Wireless Sensor Network

TL;DR

This paper develops a real-time drone path planning method for efficient data collection from wireless sensor networks, optimizing energy use and enabling quick recalculations for different drone ranges.

Contribution

It introduces a homotopy-based approach to find locally optimal drone paths that minimize energy consumption, adaptable to various drone ranges and real-time constraints.

Findings

Homotopy method produces a family of solutions for different drone ranges.

The approach enables real-time recalculation of drone paths.

The solution is globally optimal for sufficiently long drone paths.

Abstract

We consider a remote sensing system in which fixed sensors are placed in a region, and a drone flies over the region to collect information from cluster heads. We assume that the drone has a fixed maximum range, and that the energy consumption for information transmission from the cluster heads increases with distance according to a power law. Given these assumptions, we derive local optimum conditions for a drone path that either minimizes the total energy or the maximum energy required by the cluster heads to transmit information to the drone. We show how a homotopy approach can produce a family of solutions for different drone path lengths, so that a locally optimal solution can be found for any drone range. We implement the homotopy solution in python, and demonstrate the tradeoff between drone range and cluster head power consumption for several geometries. Execution time is sufficiently rapid for the computation to be performed real time, so the drone path can be recalculated on the fly. The solution is shown to be globally optimal for sufficiently long drone path lengths. For future work, we indicate how the solution can be modified to accommodate moving sensors.