Abstract
Automated maintenance has become a necessity for Unmanned Aerial Vehicle (UAV) systems to function in remote environments for an extended period of time with a higher number of vehicles. Once removed from the energy management loop, the human operator is free to concentrate on higher level task management and data analysis. This paper firstly describes the design, test and construction of an autonomous Ground Recharge Stations (GRS) for battery-powered quadrotor helicopter. In order to incorporate the charging of the quadrotors in the overall swarm behaviour, the focus of the research presented here has been to reduce the charging phase of a single vehicle by developing safer electrical contacts and using a balancer in the charging process. The amount of extra current available from the new design easily pushed the flying-time/charging-time ratio of the quadrotors over 1. The paper then describes a novel approach for the integration of this technology into an energy efficient multi-agent system. The development of a prioritisation function and queuing protocols between the UAVs and GRSs demonstrate an optimised solution to the assignment problem dependent on the mission profile. Numerical experiments show that the system’s energy management remains efficient regardless of number and position of the platforms, or nature of the environment.
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Bresciani, T.: Modelling, Identification and Control of a Quadrotor Helicopter (2008)
Bouabdallah, S.: Design and control of quadrotors with application to autonomous flying. PhD Thesis, Ecole Polytechnique Federale de Lausanne (2007)
Dale, D.R.: Automated ground maintenance and health management for autonomous unmanned aerial vehicles. MS Thesis, Massachusetts Institute of Technology (2007)
Valenti, M., Dale, D.R., How, J.P., de Farias, D.P.: Mission Health Management for 24/7 Persistent Surveillance Operations. AIAA Conference on Guidance, Navigation and Control (2007)
Amelin, K., Granichin, O.: Multiagent Network Control for the Group of UAVs. PHYSCON (2011)
Saad, E., Vian, J., Clark, G.J., Bieniawski, S.: Vehicle Swarm Rapid Prototyping Testbed. The Boeing Company, Seattle (2009)
Godwin, M.F., Spry, S.C., Hedrick, J.K.: A Distributed System for Collaboration and Control of UAV Groups: Experiments and Analysis. PHYSCON (2007)
Thunder Power, RC.: TP1430C: Single Port High-Power Multi-Chemistry Charger/Discharger/Cycler/Balancing System. Instruction/Operation Manual (2012)
Atmel Corporation: 8-bit Atmel Microcontroller with 64K/128K/256K Bytes In-System Programmable Flash. ATmega2560/V, Manual (2011)
Multi-Contact: Stäubli Group: Test & Measureline, Test Accessories for Electricians (CAT III). According to IEC/EN 61010-031:2008 (2008)
Shima, T., Schumacher, C.: Assignment of cooperating UAVs to simultaneous tasks using genetic algorithm. In: Proc. AIAA Guidance, Navigation, and Control Conference and Exhibit, San Francisco, Paper no. AIAA-2005-5829 (2005)
Ahner, D.K., Buss, A.H., Ruck, J.: Assignment scheduling capability for unmanned aerial vehicles—a discrete event simulation with optimization in the loop approach to solving a scheduling problem. In: Proc. of the 2006 Winter Simulation Conference (2006)
Zeng, J., Yang, X., Yang, L., Shen, G.: Modeling for UAV resource scheduling under mission synchronization. J. Syst. Eng. Electron. 21(5), 821–826 (2010)
Kim, Y.S., Gu, D.W., Postlethwaite, I.: Real-time optimal mission scheduling and flight path selection. IEEE Trans. Autom. Control 52(6), 1119–1123 (2007)
Shetty, V.K., Sudit, M., Nagi, R.: Priority-based assignment and routing of a fleet of unmanned combat aerial vehicless. Comput. Oper. Res. 35, 1813–1828 (2008)
Kim, J., Song, B., Morrison, J.R.: On the scheduling of systems of heterogeneous UAVs and fuel service stations for long-term mission fulfillment. J. Intell. Robot. Syst. 70(1–4), 347–359 (2013)
Kim, J., Morrison, J.R.: On the concerted design and scheduling of multiple resources for persistent UAV operations. In: Proceedings of ICUAS (2013)
Bethke, B., How, J.P., Vian, J.: Group Health Management of UAV Teams With Applications to Persistent Surveillance. American Control Conference (2008)
Lalish, E., Morgansen, K.: Distributed Reactive Collision Avoidance for Multivehicle Systems. In: IEEE Conference on Decision and Control (2008)
Johnson, D.: Efficient algorithms for shortest paths in sparse networks. J. Assoc. Comput. Mach. 24, 1–13 (1977)
Larsen, J., Pedersen, I.: Experiments with the auction algorithm for the shortest path problem. Nord. J. Comput. 6(4), 403–421 (1999)
Bertsekas, D.P.: An Auction Algorithm for Shortest Paths. Massachusetts Institute of Technology, Laboratory for Information and Decision Systems (1990)
Bertsekas, D.P.: Auction Algorithms. Massachusetts Institute of Technology, Laboratory for Information and Decision Systems
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Leonard, J., Savvaris, A. & Tsourdos, A. Energy Management in Swarm of Unmanned Aerial Vehicles. J Intell Robot Syst 74, 233–250 (2014). https://doi.org/10.1007/s10846-013-9893-8
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DOI: https://doi.org/10.1007/s10846-013-9893-8