Task scheduling system for UAV operations in indoor environment

TL;DR

This paper presents a novel UAV task scheduling methodology for indoor environments, combining a heuristic based on Earliest Available Time with Particle Swarm Optimization to efficiently generate near-optimal schedules.

Contribution

It introduces a new UAV scheduling approach that integrates heuristic algorithms with PSO for rapid, high-quality schedule creation in indoor settings.

Findings

The combined heuristic and PSO approach reduces makespan effectively.

The scheduler adapts quickly to uncertain events.

Performance tests show improved scheduling efficiency.

Abstract

Application of UAV in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space-flexibility in an occupied or hardly-accessible indoor environment, e.g., shop floor of manufacturing industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a methodology with a heuristic (based on Earliest Available Time algorithm) which assigns tasks to UAVs with an objective of minimizing the makespan. In addition, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with Particle Swarm Optimization (PSO) algorithm to find a quick near optimal schedule. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on a real flight demonstration. Performance evaluation of scheduler is discussed in detail and the best solution obtained from a selected set of parameters is reported.