A Resource Allocation System for Heterogeneous Autonomous Vehicles

UoM administered thesis: Phd

  • Authors:
  • Bilal Kaddouh

Abstract

This research aims to understand the different requirements of civilian multiple autonomous vehicle systems in order to propose an adequate solution for the resource allocation problem. A new classification of unmanned system applications is presented with focus on unmanned aerial vehicles (UAVs). The main resource allocation systems requirements in each category are presented and discussed. A novel dynamic resource allocation model is introduced for efficient sharing of services provided by ad hoc assemblies of heterogeneous autonomous vehicles. A key contribution is the provision of capability to dynamically select sensors and platforms within constraints imposed by time dependencies, refuelling, and transportation services. The resource allocation problem is modelled as a connected network of nodes and formulated as an Integer Linear Program (ILP). Solution fitness is prioritized over computation time. Simulation results of an illustrative scenario are used to demonstrate the ability of the model to plan for sensor selection, refuelling, collaboration and cooperation between heterogeneous resources. Prioritization of operational cost leads to missions that use cheaper resources but take longer to complete. Prioritization of completion time leads to shorter missions at the expense of increased overall resource cost. Missions can be successfully re-planned through dynamic reallocation of new requests during a mission. Monte Carlo studies on systems of increasing complexity show that good solutions can be obtained using low time resolutions, with small time windows at a relatively low computational cost. In comparison with other approaches, the developed ILP model provides provably optimal solutions at the expense of longer computation time. Flight test procedures were developed for performing low cost experiments on a small scale, using commercial off the shelf equipment, with ability to infer conclusions on the large-scale implementation. Flight test experiments were developed and performed that assessed the performance of the developed ILP model and successfully demonstrated its main capabilities.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date1 Aug 2017