Cherlet, Simon

Copot, Cosmin

De Keyser, Robain

English

Ghent University Academic Bibliography

Distributed control of autonomous agents with sensor fusionmethodologiesSimon CherletDySC, Ghent UniversityTechnology Park 914B-9052 ZwijnaardeSimon.Cherlet@UGent.beCosmin CopotDySC, Ghent UniversityTechnology Park 914B-9052 ZwijnaardeCosmin.Copot@UGent.beRobain De KeyserDySC, Ghent UniversityTechnology Park 914B-9052 ZwijnaardeRobain.DeKeyser@UGent.be1 IntroductionStimulated by a worldwide demand for ever larger produc-tivity in crop harvesting, agricultural manufacturers havepushed the size and power of contemporary combine har-vesters to its maximum. However this trend is now steadilysaturating due to several considerations such as cost, com-plexity, maintenance issues, and road transportation restric-tions. Because of these problems manufacturers have tocome up with other ways of making future farming evenmore cost efficient.Bearing in mind the current state-of-the-art in autonomousvehicles, beit ground or aerial [1, 2], it seems the time isright to introduce a novel approach to the aforementionedproblem. Namely, instead of using one very large, ponder-ous combine harvester, multiple somewhat smaller harvest-ing machines could be applied in such a way that only one ofthem (the leader) is being controlled by an operator and theothers follow autonomously in a certain, predefined pattern.And to obtain enhanced control performance local sensorydata on each of the harvesters (e.g. GPS, computer vision...)could be fused with information gathered by a quadrotoroverflying the formation from above [3, 4].2 Distributed multi-agent coordinationOver time a lot of research has been done on coordinatedcontrol of multiple autonomous mobile robots [5]. From acontrol engineering perspective, the goal is to compute theinputs that drive the vehicles along trajectories which main-tain relative positions as well as safe distances between eachagent while performing a certain objective. In this regardmodel-based predictive control (MPC) has proven to be verypromising due to its ability to handle complex, constrainedmultivariable systems easily and effectively. However, thecomputational effort required for the inherent optimizationscales poorly with the size of the system and can becomeprohibitive for large systems. To address this computa-tional issue when applying a centralised MPC architecture,attention has recently focused on distributed MPC (DMPC)where each subsystem (i.e. vehicle controller) solves its ownsmaller optimization problem taking into account informa-tion communicated by its neighbour [6].3 GoalsThis research project aims at developing a DMPC frame-work for formation control of multiple autonomous groundvehicles extended with aerial agents (e.g. quadrotor) actingas remote sensors. Since this is actually a networked controlsystem the framework should be able to deal with commu-nication delays and information drop outs. From a globalperspective, two major objectives are targeted:1. To develop and validate a constrained DMPC schemeon a laboratory scale application consisting of n au-tonomous ground vehicles and a quadrotor acting as aremote sensor.2. To develop a methodology for instrumentation and forvariable selection with the purpose of sensor fusion asan aid tool to improve global control.References[1] Toma´s Lozano-Perez, Ingemar J Cox, and Gordon TWilfong. Autonomous robot vehicles. Springer Science &Business Media, 2012.[2] Kimon P Valavanis and George J Vachtsevanos.Handbook of Unmanned Aerial Vehicles. Springer, 2015.[3] Andres Hernandez, Harold Murcia, Cosmin Copot,and Robin De Keyser. Towards the development of a smartflying sensor: Illustration in the field of precision agricul-ture. Sensors, 15(7):16688–16709, 2015.[4] Jairo Andres Hernandez Naranjo, Harold Murcia,Cosmin Copot, and Robain De Keyser. Model predictivepath-following control of an a.r. drone quadrotor. In Memo-rias del XVI Congreso Latinoamericano de Control Auto-matico, Proceedings, pages 618–623, 2014.[5] Yongcan Cao, Wenwu Yu, Wei Ren, and GuanrongChen. An overview of recent progress in the study of dis-tributed multi-agent coordination. Industrial Informatics,IEEE Transactions on, 9(1):427–438, 2013.[6] Hojjat A Izadi, Brandon W Gordon, and YouminZhang. Decentralized model predictive control for coopera-tive multiple vehicles subject to communication loss. Inter-national Journal of Aerospace Engineering, 2011, 2011.

Distributed control of autonomous agents with sensor fusion methodologies

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Distributed control of autonomous agents with sensor fusion methodologies

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