This paper presents Micro Aerial Vehicles (MAVs) and their cooperative systems including Unmanned Ground Vehicles (UGVs) and a Base Station (BS), which were primarily designed for the 1st US-Asian Demonstration and Assessment on Micro-Aerial and Unmanned Ground Vehicle Technology (MAV08). The MAVs are of coaxial design, which imparts mechanical stability both outdoor and indoor while obeying a 30 cm size constraint. They have carbon fibre frames for weight reduction allowing microcontrollers and various sensors to be mounted on-board for tele-operated and waypoint control. The UGVs are similarly equipped to perform their own search and tracking mission but also to support the MAVs by relaying data between the MAVs and the BS when they are out of direct range. The BS monitors the vehicles and their environment and navigates them autonomously or with humans in the loop through the developed GUI. The ability of the MAV in flight was demonstrated by showing continuous hovering. The efficacy of the overall system was validated by autonomously controlling two UGVs for cooperative search.
References
1.
HirosueW.OokuraA.SunadaS., A study of a coaxial helicopter(II): Analysis on effects of a stabilizer bar on fuselage motion, 41th Aircraft Symposium of The Japan Society for Aeronautical and Space Sciences paper, 2003, 293–296,.
2.
BourgaultF.FurukawaT. and Durrant-WhyteH. F., Coordinated decentralized search of a lost target in a Bayesian world, IEEE/RSJ International Conference of Intelligent Robots and Systems paper, 2003, 48–53.
3.
AltugE.OstrowskiJ.P. and MahonyR., Control of a Quadrotor helicopter Using Visual Feedback, IEEE International Conference on Robotics and Automation paper, 2002, 72–77.
4.
EarlM.G., and AndreaR.D., Real-time attitude estimation techniques applied to a four rotor helicopter, 43rd IEEE Conference on Decision and Control paper, 2004, 3956–3961.
5.
HowJ.P.McGrewJ.S.FrankA.A. and HinesG.H., Autonomous aircraft flight control for constrained environments, IEEE International Conference on Robotics and Automation paper, 2008, 2213–2214.
6.
ValentiM.BethkeB.DaleD.FrankA.McGrewJ.AhrensS.HowJ.P. and VianJ., The MIT indoor multi-vehicle flight testbed, IEEE International Conference on Robotics and Automation paper, 2007, 2758–2759.
7.
WangW.SongG.NonamiK.HirataM. and MiyazawaO., Autonomous control for micro-flying robot and small wireless helicopter X.R.B., IEEE/RSJ International Conference on Intelligent Robots and Systems paper, 2006, 2906–2911.
8.
AltugE.OstrowskiJ.P., and TaylorC.J., Control of a quadrotor helicopter using dual camera visual feedback, Int. J. of Robotics Research, 2005, 24, 329–341.
9.
RomeroH.BenosmanR. and LozanoR., Stabilization and location of a four rotor helicopter applying vision, American Control Conference paper, 2006, 3930–3935.
10.
TournierG.P.ValentiM. and HowJ.P., Estimation and control of a quadrotor vehicle using monocular vision and Moirè patterns, AIAA Guidance, Navigation, and Control Conference and Exhibit paper, 2006.
11.
XuC.QiuL.LiuM.KongB. and GeY., Stereo vision based relative pose and motion estimation for unmanned helicopter landing, IEEE International Conference on Information Acquisition paper, 2006, 31–36.
12.
BethkeB., Persistent vision-based search and track using multiple UAVs, Master Thesis, Massachusetts Institute of Technology, 2007.
13.
BethkeB.ValentiM. and HowJ.P., Cooperative vision based estimation and tracking using multiple UAVs, Lecture Notes in Control and Information Sciences, Advances in Cooperative Control and Optimization, 2007, 369.
14.
RudolP.WzorekM.ConteG. and DohertyP., Micro unmanned aerial vehicle visual servoing for cooperative indoor exploration, IEEE Aerospace Conference paper, 2008, 1–10.
15.
HeR.PrenticeS. and RoyN., Planning in information space for a quadrotor helicopter in a GPS-denied environment, IEEE International Conference on Robotics and Automation paper, 2008, 1814–1820.
16.
BrissetP.BronzM.GorrazM., and TylerJ., YAP (Yet Another Paparazzi), 1st US-Asian demonstration and assessment of micro-aerial and unmanned ground vehicle technology paper, 2008.
17.
ShkarayevS.MoschettaJ.M. and BatailleB., Aerodynamic design of micro air vehicles for vertical flight, J. of Aircraft, 2007, 45, 1715–1724.
18.
BachrachA.GaramifardA.GurdanD.HeR.PrenticeS.StumpfJ. and RoyN., Co-ordinated tracking and planning using air and ground vehicles, International Symposium on Experimental Robotics paper, 2008.
19.
MakL.C.WhittyM. and FurukawaT., A localisation system for an indoor rotary-wing MAV using blade mounted LEDs, Sensor Review, 2008, 28, 125–131.
20.
BourgaultF.FurukawaT. and Durrant-WhyteH.F., Process model, constraints and the coordinated search strategy, IEEE International Conference on Robotics and Automation paper, 2004, 5256–5261.
21.
WongE.BourgaultF. and FurukawaT., Optimal multi-vehicle search for multiple lost targets in a Bayesian world, IEEE International Conference on Robotics and Automation paper, 2005, 3180–3185.
22.
FurukawaT.BourgaultF.LavisB. and Durrant-WhyteH.F., Bayesian search-and-tracking using coordinated UAVs for lost targets, IEEE International Conference on Robotics and Automation paper, 2006, 2521–2526.
23.
FurukawaT.Durrant-WhyteH.F. and LavisB., The element-based method – Theory and its application to Bayesian search and tracking, IEEE/RSJ International Conference on Intelligent Robots and Systems paper, 2007, 2807–2812.
24.
LavisB.FurukawaT. and Durrant-WhyteH.F., Dynamic space reconfiguration for Bayesian search and tracking with moving targets, Autonomous Robots, 24, 2008, 387–399.
