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Distributed persistent coverage control and performance evaluation of multi-agent system

Part of: 31st Congress of The International Council of the Aeronautical Sciences (ICAS)

Published online by Cambridge University Press:  03 May 2019

S. Lee Open the ORCID record for S. Lee [Opens in a new window]  and
Y. Kim Open the ORCID record for Y. Kim [Opens in a new window]
S. Lee*
Affiliation:
Seoul National University, Department of Mechanical and Aerospace Engineering, Institute of Advanced Aerospace Technology, Seoul, Republic of Korea
Y. Kim*
Affiliation:
Seoul National University, Department of Mechanical and Aerospace Engineering, Institute of Advanced Aerospace Technology, Seoul, Republic of Korea
*
lsw7169@snu.ac.kr
ydkim@snu.ac.kr
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Abstract

The persistent coverage control problem is formulated based on cell discretisation of two-dimensional mission space and time-increasing cell ages. A new performance function is defined to represent the coverage level of the mission space, and time behaviour is evaluated by the probabilistic method based on the detection model of agents. For comparison, persistent coverage controllers are designed by a target-based approach and a reactive approach. Both controllers are designed in a distributed manner using Voronoi tessellation and Delaunay graph-based local information sharing. Numerical simulation is performed to analyse the evaluated mean age of cells and evaluated coverage level over time for the designed persistent coverage controllers. The differences between the evaluation model and simulation situation are discussed.

Keywords

Coverage controlMulti-agent systemDistributed systemPerformance evaluation
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1268 , October 2019 , pp. 1701 - 1723
Copyright
© Royal Aeronautical Society 2019 

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Footnotes

A version of this paper first appeared at the 2018 ICAS International Conference on Aeronautical Sciences, Bela Horizonte, Brazil.

References

REFERENCES

Nigam, N. The multiple unmanned air vehicle persistent surveillance problem: A review, Machines, January 2014, 2. (1), pp 13–72.CrossRefGoogle Scholar
Mohseni, F., Doustmohammadi, A. and Menhaj, M.B. Centralized receding horizon coverage control for mobile sensory networks. International Conference on Intelligent Systems Modelling and Simulation (ISMS), 8–10 February, 2012, Kota Kinabalu, Malaysiapp, pp 588–593.CrossRefGoogle Scholar
Mohseni, F., Doustmohammadi, A. and Menhaj, M.B. Distributed receding horizon coverage control for multiple mobile robots, IEEE Systems Journal, March 2016, 10, (1), pp 198–207.CrossRefGoogle Scholar
Lin, X. and Cassandras, C.G. An optimal control approach to the multi-agent persistent monitoring problem in two-dimensional spaces, IEEE Transactions on Automatic Control, June 2015, 60, (6), pp 1659–1664.CrossRefGoogle Scholar
Nigam, N., Bieniawski, S., Kroo, I. and Vian, J. Control of multiple UAVs for persistent surveillance: Algorithm and flight test results, IEEE Transactions on Control Systems Technology, September 2012, 20, (5), pp 1236–1251.CrossRefGoogle Scholar
Soltero, D.E., Schwager, M. and Rus, D. Decentralized path planning for coverage tasks using gradient descent adaptive control, The International Journal of Robotics Research, March 2014, 33, (3), pp 401–425.CrossRefGoogle Scholar
Galceran, E. and Carreras, M. A survey on coverage path planning for robotics, Robotics and Autonomous Systems, December 2013, 61, (12), pp 1258–1276.CrossRefGoogle Scholar
Schwager, M., Rus, D. and Slotine, J.-J. Decentralized, adaptive coverage control for networked robots, The International Journal of Robotics Research, March 2009, 28, (3), pp 357–375.CrossRefGoogle Scholar
Wallar, A., Plaku, E. and Sofge, D. A. Reactive motion planning for unmanned aerial surveillance of risk-sensitive areas, IEEE Transactions on Automation Science and Engineering, July 2015, 12, (3), pp 969–980.CrossRefGoogle Scholar
Schwager, M., Vitus, M.P., Powers, S., Rus, D. and Tomlin, C.J. Robust adaptive coverage control for robotic sensor networks, IEEE Transactions on Control of Network Systems, September 2017, 4, (3), pp 462–476.CrossRefGoogle Scholar
Wang, Y., Wu, S., Chen, Z., Gao, X. and Chen, G. Coverage problem with uncertain properties in wireless sensor networks: A survey, Computer Networks, August 2017, 123, pp 200–232.CrossRefGoogle Scholar
Zuo, L., Shi, Y. and Yan, W. Dynamic coverage control in a time-varying environment using Bayesian prediction, IEEE Transactions on Cybernetics, December 2017, pp 1–9. (On-line publication)Google Scholar
Lin, X. and Cassandras, C.G. An optimal control approach to the multi-agent persistent monitoring problem in two-dimensional spaces, IEEE Transactions on Automatic Control, June 2015, 60, (6), pp 1659–1664.CrossRefGoogle Scholar
Palacios-Gasos, Manuel, J. and Sagüés, C. Distributed coverage estimation and control for multirobot persistent tasks, IEEE Transactions on Robotics, 32, December 2016, (6), pp 1444–1460.CrossRefGoogle Scholar
Ramasamy, M. and Ghose, D. Learning-based preferential surveillance algorithm for persistent surveillance by unmanned aerial vehicles, International Conference on Unmanned Aircraft Systems (ICUAS), 7–10 June, 2016, Arlington, VA, pp 1032–1040.CrossRefGoogle Scholar
Cassandras, C.G., Lin, X. and Ding, X. An optimal control approach to the multi-agent persistent monitoring problem, IEEE Transactions on Automatic Control, April 2013, 58, (4), pp 947–961.CrossRefGoogle Scholar
Martinoli, A., Palacios-gasos, M., Talebpour, Z., Montijano, E. and Sag, C. Optimal path planning and coverage control for multi-robot persistent coverage in environments with obstacles. IEEE International Conference on Robotics and Automation (ICRA), 29 May–3 June, 2017, Singapore, Singapore, pp 1321–1327.CrossRefGoogle Scholar
Cortes, J., Martinez, S., Karatas, T. and Bullo, F. Coverage control for mobile sensing networks, IEEE Transactions on Robotics and Automation, April 2004, 20, (2), pp 243–255.CrossRefGoogle Scholar
Quijano, H.J. and Garrido, L. Improving cooperative robot exploration using an hexagonal world representation, Electronics, Robotics and Automotive Mechanics Conference (CERMA), 25–28 September, 2007, Morelos, Mexico, pp 450–455.CrossRefGoogle Scholar