UAV helicopter relative state estimation for autonomous landing on moving platforms in a GPS-denied scenario

Proceedings of the 2015 IFAC Workshop on Advanced Control and Navigation Aerospace Proceedings of for theAutonomous 2015 IFAC Workshop on Vehicles Advanced Control Proceedings of for theSeville, 2015 IFAC Workshop on Vehicles Advanced Control JuneNavigation 10-12, 2015. Spain and Autonomous Aerospace Available online at www.sciencedirect.com and forSeville, Autonomous JuneNavigation 10-12, 2015. Spain Aerospace Vehicles June 10-12, 2015. Seville, Spain

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48-9 (2015) 037–042 UAV helicopter relativeIFAC-PapersOnLine state estimation for autonomous landing on moving UAV helicopter relative state estimation for autonomous on moving platforms in a GPS-denied scenario landing UAV helicopter relative state estimation for autonomous landing on moving platforms in a GPS-denied scenario platforms in a GPS-denied scenario Francisco Alarcon, Daniel Santamaria,

Francisco Alarcon, Daniel Santamaria, Antidio Viguria. Francisco Alarcon, Daniel Santamaria, Antidio Viguria. Antidio Viguria. Center for Advanced Aerospace Technologies (CATEC), for Advancede-mail: Aerospace Technologies (CATEC), Seville, Spain (Tel:Center +34-954-179-002; {falarcon/dsantamaria/aviguria}@ catec.aero). for Advancede-mail: Aerospace Technologies (CATEC), Seville, Spain (Tel:Center +34-954-179-002; {falarcon/dsantamaria/aviguria}@ catec.aero). Seville, Spain (Tel: +34-954-179-002; e-mail: {falarcon/dsantamaria/aviguria}@ catec.aero). Abstract: The objective of this work is to describe an innovative relative position and velocity state Abstract: objective this work to describe relative positionlanding and velocity state estimationThe approach for of rotatory wingisUAV linked an withinnovative a rope. An autonomous of a UAV Abstract: work isUAV to describe an relative position and velocity state estimation approach rotatory wing linked withinnovative a rope. autonomous of aofUAV helicopter The in aobjective static for andof inthis mobile platform in absence of GPS An are presented as landing application this estimation approach for rotatory wing UAV linked with a rope. An autonomous landing of a UAV helicopter a static andestimation in mobileisplatform absence of GPS are presented as application of this technology.inThe relative obtained in using a device that provides rope orientation, and tension helicopter inan a altimeter static andand in an mobile in absence GPS are using presented as application of this technology. The relative estimation isplatform obtained using a device that provides rope orientation, and tension information, IMU. All this information isofcombined a sensor fusion strategy. technology. The relative estimation is obtained using a device that provides rope orientation, and tension information, an altimeter and an IMU. All this information is combined using a sensor fusion strategy. Keywords: Navigation systems, autonomous vehicles, fusion, rotatory © 2015, IFAC of Automatic Control) by Elsevier Ltd.sensor Allfusion rights reserved. information, an(International altimeter andFederation an autonomous IMU. All thislanding, information isHosting combined usingUAV, a sensor strategy. Keywords: systems, autonomous landing, autonomous vehicles, UAV, sensor fusion, rotatory wing UAV,Navigation GPS denied. Keywords: systems, autonomous landing, autonomous vehicles, UAV, sensor fusion, rotatory wing UAV,Navigation GPS denied. wing UAV, GPS denied.   the image processing is fused with the relative position and 1. INTRODUCTION  the image processing is fused the relative and velocity solution obtained with with a differential GPSposition with carrier 1. INTRODUCTION image processing fused the relative position and velocity solution obtained with with acommunication differential GPSlink withbetween carrier phase measurements; aiswireless During the last decade, UAV related research has increased the 1. INTRODUCTION velocity solution obtained with a differential GPS with carrier a wireless communication link between During the lastwithin decade, related has increased the twomeasurements; aircrafts is used to transmit the measurements of their exponentially theUAV military and research civilian because these phase a wireless communication link between During lastwithin UAV has increased the twomeasurements; aircrafts is used to transmit the measurements of their exponentially military and research civilian because these phase sensors (Williamson, et al., 2009). vehiclesthe offer a decade, wide the range of related practical applications. (Maza, the two (Williamson, aircrafts is used to transmit the measurements of their exponentially within the military and civilian because these sensors et al., 2009). vehicles offer a wide range of practical applications. (Maza, Other type of vision algorithms are based on the et al., 2011) and (Xiang & Tian, 2007). et al., algorithms 2009). vehicles offer a wide range of practical applications. (Maza, sensors (Williamson, type ofof vision based platform. on the et 2011) and (Xiang & Tian, implementation known patterns in thearereference In al., recent years researchers have2007). intensively worked in the Other Other type of vision algorithms are based on the the et al., 2011) and (Xiang & Tian, 2007). implementation of known patterns in the reference platform. In recent years researchers have intensively worked in the development of controllers for the different flight phases of In these cases the obtained images are compared with implementation of known patterns in the reference platform. In recent years researchers have intensively worked in the In thesepatterns cases theandobtained images are compared the development of controllers for the different flight phases of known it is possible to obtain the with relative the autonomous helicopter missions. These phases could be In thesepatterns cases the images are compared the development controllers for thehovering, different flight phases of known andobtained it(Matthias is possible to obtain relative the autonomous helicopter missions. These phases couldand be position between them Faessler, s.f.). the with classified in ofvertical takeoff, navigation known patterns and it is possible to obtain the relative the autonomous helicopter missions. These phases could be (Matthias Faessler, s.f.). classified in vertical takeoff, hovering, navigation and position Recent between studies them related with innovative landing control landing (Fang, et al., 2008). Because autonomous helicopters position them (Matthias Faessler, s.f.). classified innonvertical takeoff, hovering, navigation and techniques Recent between studies related innovative landing control landing (Fang, et lineal al., 2008). Because autonomous helicopters have shown thatwith helicopter linked with the landing are highly systems, a lot of researchers have Recent studies related control landing (Fang, et lineal al.,in2008). Because autonomous helicopters shown thatwith helicopter linkedoflanding with landing are highly systems, a control lot of problems researchers have techniques platform byhave a rope improves theinnovative stability the the vehicle by focused theirnon work the dynamic (Wang, techniques have shown that helicopter linkedofwith the landing are highly non lineal systems, a lot of researchers have platform by a rope improves the stability the vehicle by focused their work in the dynamic control problems (Wang, et al., 2009). One of the most important conclusions is the applying a certain level of tension in the rope(Sandino, et al., platform bycertain a ropelevel improves the stability of the vehicle by focused their work in the the most dynamic controlconclusions problems (Wang, applying a of tension in the rope(Sandino, et al., et al., 2009). One of important is the importance of using adequate sensors for the different phases 2014). The work presented here uses the same configuration applying a certain level of tension in the rope(Sandino, et al., et al., 2009). One of the most important conclusions is the 2014). The work presented here uses the same configuration importance for the different obtaining an accurate relative position and velocity of the of flight inof using order adequate to solvesensors the control problems.phases The for 2014). The work presented here uses the same configuration importance of using adequate sensors for the different phases for obtaining an accurate relative position and velocity the of flight in order to solve the control problems. The RUAV with respect to the attached point of the rope.ofThis autonomous landing is the most complex and critical phase for obtaining andepends accurate relative velocity the of flighta high in landing order to the solve The with respect to the attached pointand of systems: the rope.ofThis autonomous mostthe complex andproblems. critical phase measurement only onposition on-board rope because level is of accuracy andcontrol reliability is needed in RUAV RUAV with respect to the attached point of the rope. This autonomous landing is the most complex and critical phase depends measurement only on on-board systems: rope because high levelNavigation of accuracy and reliability is needed in measurement orientation, altitude (or rope length), the GNCa(Guidance and Control) solution. depends only and on on-board systems: rope because a(Guidance high levelNavigation of accuracy and reliability needed in measurement orientation, measurement (or rope the GNCto and Control) magnetometer altitude measurements acceleration and length), angular In order implement a navigation system forsolution. an is autonomous orientation, altitude measurement (or rope length), the GNC (Guidance Navigation and Control) solution. magnetometer measurements and acceleration angular In order to navigation for an autonomous data from an Inertial Measurement Unit and (IMU). This landing onimplement a mobile aplatform, it system is necessary to have the velocity magnetometer measurements and acceleration and angular In order to aplatform, navigation system forand an autonomous data from Inertial Measurement (IMU). This landing on amultiple mobile sensors is necessary to have the velocity relative position isancompletely independentUnit from the GPS, feedback ofimplement for itmeasuring determining data fromisancompletely Inertial Measurement Unit (IMU). This landing on amultiple mobile sensors platform, itmeasuring is necessary to have the velocity relative position independent from the GPS, feedback of for and determining the state of the vehicle: position, velocity and attitude. The and represents a,cheap and reliable alternative positioning relative position is completely independent from the GPS, feedback of multiple sensors for measuring and determining represents a,cheap and alternative positioning the state of used the vehicle: position, velocity attitude. The system for tethered RUAV or reliable multicopters. commonly navigation strategy is aand combination of and and represents a,cheap and reliable alternative positioning the state of the vehicle: position, velocity and attitude. The system for tethered RUAV or multicopters. commonly used navigation strategy is a combination of This paper is organized as follows. In the next section the global satellite navigation systems (GNSS) and inertial system for tethered RUAV or multicopters. commonly used navigation strategy is a combination of paper is organized as follows. is In presented. the next section the global satellite navigation systems (GNSS) inertial problem description and formulation In Section measurement units (IMU). Using sensor fusion and strategies as This is organized as follows. In the next the global satellite navigation systems (GNSS) and inertial problem description and formulation presented. In required Section measurement units (IMU). Using sensor strategies as This III a paper detailed explanation of the is systems andsection the Kalman filter (Solimeno, 2007) thisfusion combination can problem description and formulation is presented. In Section measurement units (IMU). Using sensor fusion strategies as III a detailed explanation of is thedevoted systemsto and required the Kalman filter accuracy (Solimeno, 2007) this combination can hardware is given. Section IV describe the provide a position of some meters. However, a more III a detailed explanation of is thedevoted systems and required the Kalman filterisaccuracy (Solimeno, 2007) this combination can hardware isfrom given. Sectiondescription IV describe the provide asolution position of meters. However, a more algorithms, a general to the to implementation accurate required forsome landing purposes. hardware isfrom given. Sectiondescription IV is devoted to describe the provide a position accuracy of some meters. However, a more algorithms, a general to the implementation accurate solution is required for landing purposes. Between the different strategies for improving the accuracy of the global and relative navigation algorithms. Two accurate solution is required for landing purposes.the accuracy of algorithms, fromresults a general description to the the global and relative navigation algorithms. Between the different strategies improving flight of the technology hereimplementation describedTwo are of the relative navigation, it isforpossible to find lot of different Between the different strategies for improving the accuracy of the global and relative navigation algorithms. different flight results of the technology here described are of the relative navigation, it is possible to find lot of shown in section V. The first experiment in Section Two V.a researches that use electro-optical sensors for obtaining different flight results of thefirst technology hereindescribed are of the relative navigation, it is possible to find lot of shown in section V. The experiment Section V.a researches that use electro-optical sensors for obtaining presents a comparison between the relative positioning relative position solutions through image processing (Bagen, shown in section V. The first experiment in Section V.a researches that use electro-optical sensors for obtaining presents aand comparison between thein relative relative position solutions through the RTK GPS solution a landingpositioning maneuver et al., 2009). Another example is inimage (Hsia, processing et al., 2012)(Bagen, where estimation presents comparison between relative positioning relative position solutions through processing (Bagen, estimation and the RTK solution a landing maneuver et al., 2009). example is inimage (Hsia, et al.,by 2012) where the aattached point GPS of the ropetheisinnot moving. Section the altitude isAnother obtained with respect the ground fusing the when estimation and RTK GPS solution innot a landing maneuver et al., 2009).isAnother example is in (Hsia, etprovided al.,by 2012) the attached pointestimation of the rope moving. Section the altitude obtained with the ground fusing the when V.b. shows the the relative of is an autonomous RUAV GPS measurements with therespect stereo vision bywhere two the attached point of the (where rope not moving. Section the altitude is obtained with respect the ground fusing the when V.b. shows relative estimation of is anthe autonomous RUAV GPS measurements the stereomethods vision provided bybeen two that tracks athemoving platform rope is attached) cameras. Also therewith are fusion thatbyhave V.b. shows the relative estimation of an autonomous RUAV GPS measurements with the stereo vision provided by two that tracks a moving platform (where the rope is attached) cameras. thererefuelling are fusion methods that Inhave developed Also for flight of two aircrafts. this been case and it finalizes with a successful landing. that tracks a moving platform (where the rope is attached) cameras. Also there are fusion methods that have been developed for flight refuelling of two aircrafts. In this case and it finalizes with a successful landing. developed for flight refuelling of two aircrafts. In this case and it finalizes with a successful landing. Copyright © 2015, 2015 IFAC 37 Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International Federation of Automatic Control) Copyright 2015 responsibility IFAC 37 Control. Peer review©under of International Federation of Automatic Copyright © 2015 IFAC 37 10.1016/j.ifacol.2015.08.056

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2. PROBLEM DESCRIPTION AND FORMULATION

Initial RUAV platform configuration The experiments here presented were performed with a RC platform Aerotech Cb-5000 that was converted into a RUAV. The diameter of its rotor is 1.8m and the rotor speed approximately 1300 rpm. The aircraft was equipped with a PC104 processing unit, a NAV 440 system that provides GPS and IMU measurements and a Crossbow magnetometer in order to have an accurate magnetic heading measure.

Three different reference frames are considered in this work. Body frame: The body frame is a non-inertial coordinate system associated with the vehicle with the origin at its center of gravity. The xaxis points in the forward direction, the z-axis down through the vehicle and the y-axis completes the right-hand coordinate system. This frame will be denoted by the superscript b.

Specific RUAV tether-based configuration. In order to incorporate a relative localization and moving platform landing capabilities, the RUAV described before was equipped with the following additional sensors:  A ROKE MRII laser altimeter. This sensor provides measurements of the relative altitude between the RUAV and the landing place with centimetre accuracy.  A specific cardan joint device was created. This device is shown in Figure 2 and it is composed of 2axis coupled cardan joints equipped with magnetic encoders attached to each axis. This device is used to estimate the angles between the rope and the helicopter frame in terms of 2 successive rotations of the cardan joint. It has also a security rope release system and a load sensor to measure the tension level of the rope  A centimetre precision RTK GPS positioning system was integrated into the RUAV. Its measurements were used as a reference of the positioning accuracy obtained by the relative rope estimation algorithms here presented and were not used for control purposes.

Inertial navigation frame This is an inertial coordinate system oriented in the north, east and down direction. It is determined by fitting a tangent plane to the geodetic reference ellipsoid at fixed point. This point is taken as the origin of the coordinate system. The xaxis points to the true North, the y-axis points the West and the z-axis points up. This frame will be denoted by the superscript n. Device frame: The tether frame is a non-inertial coordinate system associated with the cardan joint mechanism. It has its origin in the point where the tether is connected with the helicopter. x and y axes rotates with respect to the fuselage of the helicopter and the z axis is always pointing towards the landing point. This frame will be denoted by the superscript t. This frame is shown in Figure 1 together with the body axes frame.

Figure 1: Tether frame representation. GNC approaches: Two different GNC strategies are considered in this paper for the different flight phases to be accomplished and the type of sensor information that is required to perform the mission in a safe way. The first GNC approach is the most commonly used for UAS for static platform take off and waypoint navigation in absolute coordinates. Using this approach it is possible to accomplish the mission requirements without centimeter position accuracy fusing INS/GPS information. A second GNC approach is used for the landing phase, which requires a high accuracy, or when a hovering is required around the moving landing platform in a GPS-denied scenario. This GNC approach is based on the rope sensors installed onboard the helicopter.

Figure 2: Cardan joint device. 3.2 Moving platform architecture Additionally to the RUAV onboard equipment a moving landing platform equipped with dedicated rope winding up device was developed (Figure 3).

3. SYSTEM DESCRIPTION In this section all the different components of the onboard sensors and ground equipment used in the experiments are described. 3.1 RUAV and Onboard tether sensors Figure 3: Moving platform 38

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The moving platform is based on a modified trailer equipped with a modular landing pad structure of 3 by 3 meters and a central box that contain all the equipment related with the rope winding up mechanism. 4. NAVIGATION ALGORITMHS DEVELOPMENT 4.1 GNC blocks description GNC algorithms are separated in blocks attending of the functionality that implement. The Estimator or Navigation block implements all the absolute and relative UAV state estimation. The Guidance Block contains the state machine with the logic to guide the UAS during the different phases of flight and provides the position references. This block also generates the signal that triggers the GNC strategy from using absolute information to relative and vice versa. Finally the control algorithms of the UAV are contained in the Controller block. The general scheme of the GNC architecture is shown in Figure 4.

Figure 5: Loosely coupled scheme. 4.3 Relative positioning algorithm fusing INS/tether/altimeter data The relative positioning estimator is used in the landing phase of the autonomous helicopter on static or mobile platforms. This is a critical manoeuvre that endangers the safety of the vehicle, the landing site and staff that could be in the surrounding. Therefore, a technique to estimate the position in real time with high accuracy is needed in order to successfully accomplish an autonomous landing safely. This system uses the data provided by the tether system (angles α, β and Tension T), the altitude of the altimeter (halt), the accelerations and angular velocities of the INS (ab, ω) and the magnetic field measurements of the magnetometer (mb). This information is processed and transformed to the navigational axes. The sensor fusion algorithm is then fed with the Euler angles of the helicopter (ϕ, ϴ, ψ), the measured relative position (Prelm) and the accelerations in the navigational frame (an). This scheme is shown in Figure 6.

Figure 4: GNC architecture. 4.2 Absolute positioning fusing GPS/INS EKF/magnetometer data. In the takeoff and navigation phases of the helicopter a global positioning estimator is used. The purpose of this system is to combine the data of the GPS, INS and others sensors in an optimal way to obtain a navigation system with both higher update rate and smaller position error than the standalone GPS-receiver. In this project a loosely coupled scheme that fuses the INS, GPS and magnetometer is used. In a loosely coupled integrated system, the GPS receiver has its own Kalman filter. This filter calculates the user position and velocity by processing the pseudorange and Doppler measurements that have been measured by the GPS chip. The differences between the INS and GPS calculated positions and velocities are utilized as measurements for a second Kalman filter in which INS error dynamics equations are used as system model. In this way, this second filter is able to provide estimations of all the observable INS errors, which are consequently used to correct the IMU raw measurements and to compensate the system output. In Figure 5 the scheme used in this navigation algorithm is shown.

Figure 6: Relative Positioning Architecture For understanding the architecture, the main blocks of the estimator are explained below. AHRS The Attitude and Heading Reference System (AHRS) is the module in charge of calculating the attitude of the UAV. The attitude is defined as the inclination of its body-axes reference frame to the navigation reference frame which in this project is defined as a NWU (North West Up) Local Tangent Plane reference frame. The AHRS algorithm uses the angular rate measurements from the gyroscopes to update the previous attitude estimation. After updating the previous attitude solution with the gyroscope measurements, this estimation is corrected using an Extended Kalman Filter (EKF). The goal of this Kalman Filter is estimating the attitude errors in the current attitude estimation to remove them. The measurements of the attitude errors of the current attitude estimation are obtained using the measurements provided by 39

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the accelerometers and the magnetometer. The magnetometer measurements are used to calculate the yaw of the UAV and the difference between this value and the current estimation is used as the measurement of the yaw error. In a similar way, the outputs of the accelerometers are used to calculate the measurements of the roll and pitch errors. Conversion This block is in charge of calculating the relative position of the landing point to the helicopter. This is done through the measurement of the tether system, the altimeter and the results of the AHRS. In Figure 7 a graphical description of the landing scenario with all the components that take part in the conversion block is shown.

Sensor Fusion This block is in charge of fusing the measures of attitude, acceleration and relative position. The algorithm implemented is a Kalman filter that runs at 100 Hz and calculates a solution for the relative position and velocity between the UAV and the landing point. The model implemented in the Kalman filter uses relative kinematics equations. It assumes that the acceleration of the landing platform is not too big so it can be modelled as an unknown bias with a Gaussian noise. The Kalman matrices of the filter are weighted depending on the tension of the tether T. The outputs of this block will feed the controller of the helicopter providing the attitude of the UAV and a very accurate solution for the relative position and velocity.

5.EXPERIMENTAL FLIGHT RESULTS Several experiments have been performed during 2014 in order to test the technology developed for the autonomous landing based on the tether system. In these experiments all the guidance, control and navigation modules of the autopilot were involved. These experiments were focused in the landing procedure and their relative control and estimation modules. Figure 8 shows the helicopter and the mobile platform used in the experiments. Figure 7: Landing scenario components. The first step in this block is to obtain the relative position measurement in the contact point (CP) of the tether with the fuselage. The CP of the vehicle and the altimeter are not placed at exactly the same position. This spatial separation causes small differences in position and velocity due to the lever-arm effect. In this landing phase it is necessary to have as much accuracy as possible; the measurement of the altimeter must be corrected according to equations (1) and (2): (1) (2) Once the altitude to the contact point has been calculated, it is possible to obtain the position relative to the landing point in the navigation axes. This can be done by using the equation (3) where Pt is the position vector in the tether frame. (3) In the tether frame the landing point is in the Z axis so their coordinates are [0, 0, zt]. So that (3) can be written as:

Figure 8: Experiment Setup 5.1 Static platform experiment The objective of this experiment was to validate that the solution of the relative estimator allows the UAV to land safely in a static platform. Experiments were performed with different environmental conditions, for example windy conditions.

(4) Coordinates in the navigation frame are obtained by using (4) in (5), (6) and (7). (5) Figure 9: Landing in a static platform. The helicopter took off with the rope already attached but with a low level of tension commanded into the winding up control system. Once the helicopter was flying in autonomous mode close to the attaching point, the operator commanded the control system to increase progressively the tension level up to 20N. In Figure 10 and Figure 11 the position outputs of the relative estimator are compared with

(6) (7) Once the relative coordinates have been calculated, the result has to be translated to the centre of mass of the vehicle. For doing that, it is used again a lever arm correction. (8) This result is used as the measurement of the relative position and it will feed the sensor fusion block. 40

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the RTK-GPS logged during a landing manoeuvre. It is possible to see that the estimated relative position is very accurate, and even in the vertical plane, it is very difficult to differentiate between the solutions of the RTK GPS in comparison with this new estimation approach.

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Velocity X North Y West Altitude RMS error (m/s) 0.0773 0.0725 0.0557 STD 0.1971 0.1604 0.0756 Table 2: Velocity RMS error and standard deviation media.

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These set of experiments were conducted to validate that the solution of the relative estimator allows the UAV to land safely in a mobile platform. Experiments were performed with different environmental conditions and velocities of the landing platform which was towed by a 4x4. Figure 9 shows a snapshot of the experiments.

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Figure 11: Vertical Position Root mean square error and the standard deviation of the relative position calculated with the estimator are shown in Table 1. Positioning errors are below 20 cm, hence the accuracy is good enough for landing perfectly in the platform in absence of GPS. Position X North Y West Altitude RMS error (m) 0.1467 0.1816 0.0043 STD 0.3451 0.3894 0.0067 Table 1: RMS error and Standard Deviation Media of Position

Figure 14: Mobile platform experiment In these experiments, only the UAV RTK position measurements were available and there was not any positioning information of the moving platform.

The velocity outputs of the relative estimator are compared with the RTK-GPS measurements logged during the experiment in Figure 12 and Figure 13. The solution obtained by using the tether system is also very accurate for the velocity. In Table 2 the calculated errors are shown. 2

Figure 15 shows the results of the relative estimator and the measurements logged from the RTK receiver of the UAV using 2 different scales and colours

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Figure 15: Moving platform experiment, Horizontal position 41

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In the second 60 the landing platform started to move as it is possible to see in RTK position graph (blue line). Then the relative controller was able to follow the platform and kept a horizontal position over the landing point by using the relative estate estimation (green line). It can be observed that during the autonomous landing the relative controller was able to keep the relative position under over the landing point.

Future work will study the possibility of implement a tightly coupled algorithm where all the sensors will be filtered by a unique algorithm. Also, in the following experiments a vertical movement and a roll and pitch angular movements are going to be added to the moving platform. This setup will simulate the movements of the ship deck and allows us to have a more realistic environment to validate the GNC approach for landing in complex moving platform scenarios. REFERENCES Bagen, W., Hu, J. & Xu, Y., 2009. A vision-based unmanned helicopter ship board landing system. s.l., s.n. D. Santamaría, F. A. A. J. A. V. A. O. M. B., 2011. Modelbased design, development and validation for UAS critical software. s.l.:ICUAS (International Coference on Unmanned Aircraft Systems). Fang, Wu & Li, 2008. Cntrol system design and flight testing for a miniature unmanned helicopter. s.l., s.n. Hsia, K.-H., Lien, S.-F. & Su, J.-P., 2012. Stereo Matching Method and Height Estimation for Unmanned Helicopter. In: Machine Vision - Applications and Systems. National Yunlin University of Science & Technology: Fabio Solari, Manuela Chessa and Silvio P. Sabatini. Johnson, L., Herwitz, S., Dunagan, S. & al., B. L. e., 2003. Collection Of Ultra High Spatial And Spectral Resolution Image Data Over California Vineyards With A Small UAV. Honolulu, s.n. Matthias Faessler, E. M. K. S. a. D. S., n.d. A Monocular Pose Estimation System based on Infrared LEDs. s.l., s.n. Maza, I. et al., 2011. Experimental Results in Multi-UAV Coordination for Disaster Management and Civil Security Applications.. Journal of Intelligent and Robotic Systems, 61(1), pp. Pp. 563 - 585. Merino, L. et al., 2011. An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement.. Journal of Intelligent and Robotic Systems.. O, S.-R.et al., 2006. Approaches for a Tether-Guided Landing of an Autonomous Helicopter. IEEE TRANSACTIONS ON ROBOTICS, 22(3). Sandino, L., Béjar, M., Kondak, K. & Ollero, A., 2014. Advances in Modeling and Control of thetered Unmanned helicopters to enhance hovering performance. Journal of Intelligent & Robotic Systems, Volume 73, pp. 3-18. Solimeno, A., 2007. Low-Cost INS/GPS Data Fusion with Extended Kalman Filter for Airbone Applications.. s.l.:Universidade Técnica de Lisboa. Templeton, T. R., 2007. Autonomous Vision-based Rotorcraft Landing and Accurate Aerial Terrain Mapping in an Unknown Environment, s.l.: Electrical Engineering and Computer Sciences University of California at Berkeley. Wang, C. et al., 2009. An adaptive system identification method for a micro unmanned helicopter robot. s.l., s.n. Williamson, W. R., Glenn, G. J., Dang, V. T. & Speyer, J. L., 2009. Sensor Fusion Applied to Autonomous Aerial Refueling. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS, 32(1). Xiang, H. & Tian, L., 2007. An Autonomous Helicopter System For Aerial Image Collection. s.l., s.n.

Figure 16 shows the altitude measurements logged in field and the different phases of the experiment. At the beginning the helicopter was hovering. When the moving platform started to move, the helicopter started to follow the moving platform. Once the UAV was located over the landing platform, it was commanded to land and it started to descend. Finally the UAV landed on the moving platform successfully.

Figure 16: Moving platform experiment, Altitude. Figure 17 shows the relative velocity estimation versus the RTK measurement of the mobile platform. It can be seen that when the platform started the movement, the relative velocity remained around 0. This is because the helicopter followed the platform aided by the rope. 2

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Figure 17: Moving platform experiment, Horizontal velocity Several fully autonomous moving platform landings were performed in an accurate and safe way using the relative control and estimation approach (a summary of these experiments are shown in http://youtu.be/77RMk8WEb78). CONCLUSIONS AND FUTURE WORK An innovative relative navigation system based on a tether system has been presented. Experiments have shown that the relative fusion algorithm is able of providing centimetre accuracy in absence of GPS and the solution allows the UAV control system to carry out a safe landing on static and moving platforms. These results are very promising since they offer an alternative positioning method to GPS, and also the capability to land autonomously on a moving platform in environments where the GPS satellite signals are degraded, jammed or spoofed. 42