REDUAS 2019 Paper Abstract

Paper TuD26T2.4

Autenrieb, Johannes (Cranfield University), Strawa, Natalia (Cranfield University), Shin, Hyo-Sang (Cranfield University), Hong, Ju-Hyeon (Cranfield University)

A Mission Planning and Task Allocation Framework for Multi-UAV Swarm Coordination

Scheduled for presentation during the Regular Session "Networked Swarms" (TuD26T2), Tuesday, November 26, 2019, 16:40−17:00, Room T2

2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS), November 25-27, 2019, Cranfield University, Cranfield, UK

This information is tentative and subject to change. Compiled on April 24, 2024

Keywords Networked Swarms, Autonomy

Abstract

This paper presents a multi-agent mission planning and task allocation framework designed to coordinate autonomous aerial vehicles engaged in a competition scenario. The development was a part of an inter-university UAV Swarm competition that was supported by BAE Systems. The proposed centralised system was developed with the main objectives of robustness and scalability. The system consists of a general mission planning module which decomposes the overall mission into identified sub-stages to achieve the overall mission goal. In order to enable autonomous defence actions a dynamic task allocation approach is proposed. The dynamic task allocation is using received information of detected enemies and utilises the information for a further combinatorial optimisation problem. In this work, we discuss the structure of the framework and present results obtained in a high-fidelity simulation environment. Moreover, a comparative study of the performance of three different optimization algorithms for the given combinatorial problem, namely Kuhn-Munkres, Jonker-Volgenant and Gale-Shapley, implemented in the system is included. The results demonstrate that the best allocation result performances, in terms of minimal costs, are obtained with utilising, both Kuhn-Munkres or Jonker-Volgenant methods, while the Gale-Shapley algorithms have benefits in terms of time efficiency for cases in which minimal costs are not the highest priority.