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- 1. Control of CooperativeUnmanned Aerial VehiclesKostas AlexisDepartment of Electrical& Computer Engineering,University of Patras, Greece1
- 2. Structure of this presentationControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 21. Introduction2. Quadrotor Modeling Approach3. Quadrotor Design4. Quadrotor Control Approaches5. Cooperation of UAVs6. Conclusions and Future Work
- 3. History of UAVsSome of the most wide-spread UAV designs areinspired from old mannedaircraft designs that did notconvince the market in theirtimes.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras1. Introduction3 Unmanned Aerial Vehicles can be tracked backto the beginnings of 20th century operating inmilitary missions. UAVs started as remote piloted vehicles butdue to technological and scientificadvancements autonomous systems becamefeasible. The end of 2oth century was a turning point inthe history of robotics: production expandedmassively to domestic use. Currently UAV designs for civilian applicationsmainly focus in miniaturizing existing fixed-wing and rotorcraft designs.
- 4. Scientific MotivationUnmanned Aerial Vehicles and specially quadrotor rotorcrafts pose significantscientific and engineering challenges: Very aggressive nonlinear, underactuated dynamics. They are affected from complex aerodynamic phenomena. Prone to perturbations due to atmospheric turbulence. Low-cost miniaturized sensor estimation systems are noisy, they drift and arevery prone to vibrations. Hard actuation constraints in terms of dynamic range, precision, responsetime, nonlinear characteristics and relatively high power consumption. Despite their small size they are still complex machines that need increasedcomputational power.Design and autonomous control of such systems is still an open challenge!Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras1. Introduction4
- 5. Socioeconomic MotivationUnmanned Aerial Vehicles (and specially quadrotors)can be utilized in a wide set of real-life applications: Intelligence, Surveillance, Reconnaissance (ISR) Wild-fire surveillance Agricultural services Search & Rescue Buildings inspection Area Exploration & Mapping Military applicationsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras1. Introduction5
- 6. State of the ArtResearch groups around the worldhave achieved very promising resultsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras1. Introduction6
- 7. Contributions of this work [1/2]The Contributions of this work reside in the following areas:1. Modeling of the quadrotor dynamics using Piecewise Affine systems: untilnow linear and nonlinear models of the quadrotor dynamics have been proposed.Piecewise Affine systems-based modeling provides the opportunity to:a. capture some of the nonlinearities and couplings of the system - cover arelatively large part of the system’s flight envelope.b. utilize linear control theory.2. UPATcopter Design: The UPATcopter is an efficient and modular quadrotrorexperimental platform emphasizing in the areas of:a. powerful onboard computational capabilitiesb. autonomous indoor state estimation based on inertial measurements, sonar data andvision sensorsc. extended communication optionsd. low-cost but efficient actuators.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras1. Introduction7
- 8. Contributions of this work [2/2]3. System Control: Three different Control strategies were designed andexperimentally verified for their performance:a. A Switching Model Predictive Controller for the 6-Degrees of Freedom trajectory controlof the quadrotor.b. A Constrained Finite Time Optimal Controller for the quadrotor’s attitude controlproblem.c. A Proportional-Integral-Derivative-2ndDerivative /Proportional-Integral-Derivativecontroller for the quadrotor’s rotational/translational motion dynamics. This controlaugments classical PID controllers with angular acceleration feedback.4. UAV cooperation: two cooperation strategies have been proposed in order toaddress the problems of a) cooperative forest fire surveillance and b) areaexplorationControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras1. Introduction8
- 9. Quadrotor Modeling Approach91. Introduction2. Quadrotor Modeling Approach3. Quadrotor Design4. Quadrotor Control Approaches5. Cooperation of UAVs6. Conclusions and Future Work
- 10. Modeling AssumptionsQuadrotor’s Forward Motion: difference in the liftproduced from the front and rear rotorsQuadrotor’s Sideward Motion: difference in the liftproduced from left and right rotorsQuadrotor’s Yaw motion: difference in thecounter-torque between the counter-rotatingrotor pairsPerpendicular motion: rotors’ overall thrustDynamics modeling assumptions:1. Rigid and symmetrical structure.2. CoG and Body Fixed Frame coincide.3. Rigid propellers.4. Thrust and drag forces proportional to thesquare of propeller’s speed.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras2. Quadrotor Modeling Approach10
- 11. Forces & Moments acting on the craft [1/2]Newton-Euler FormulationF: force vector on the CoMτ: total torque acting about the CoMI3x3: indentity matrixI: Inertia moment about the CoMm: total mass of the bodyV: acceleration of the CoMω: angular velocityCoM: Center of MassControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 112. Quadrotor Modeling ApproachRotation Matrix from BFF to EFF:Transformation of craft ratesexpressed in BFF and EFF:
- 12. Forces & Moments acting on the craft [2/2]Main aerodynamic forces and moments:1. Thrust force: the resultant of the vertical forces acting on all blade elements2. Hub force: the resultant of the horizontal forces acting on all blade elements3. Drag moment: the moment about the rotor shaft due to aerodynamic forces4. Rolling moment: the moment produced in forward flight when the advancing blade isproducing more lift than the retreating oneControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 122. Quadrotor Modeling ApproachGround Effect: when a rotorcraft is operating very close to ground (half of arotor’s diameter) experiences thrust augmentation due to the interferenceof the surface with the airflow pattern of the rotor system.
- 13. Piecewise Affine Modeling Approach [1/3]Euler-Lagrange formulation 6DOF Quadrotor Dynamics ModelingControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 132. Quadrotor Modeling ApproachThe angles φ,θ,ψ are independent of the translational motionAltitude motion dynamics can be decoupled from horizontalmotion dynamics
- 14. Piecewise Affine Modeling Approach [2/3]Attitude Piecewise Affine representationsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 142. Quadrotor Modeling ApproachDiscrete Time ExpressionPiecewisemodelingAugmented with Integral TermsDisturbance effects inattitude rates.
- 15. Piecewise Affine Modeling Approach [3/3]Vertical Piecewise Affine Error Dynamics Translational Piecewise Affine Error DynamicsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 152. Quadrotor Modeling ApproachAugmented with Integral TermsAugmented with Integral TermsPiecewisemodeling
- 16. Aerodynamic EffectsIn classical quadrotor modeling the aerodynamic effects due to variation of theairstream are neglected. However, even at moderate translational velocities orfor moderate wind-gusts, their impact becomes important.1. Blade FlappingControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 162. Quadrotor Modeling Approach2. Total Thrust variation3. Airflow Disruption
- 17. Simulink ModelBased on experimental measurements and CAD/CAM computation a MATLAB-Simulink model was derived in order to aid the control design process.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 172. Quadrotor Modeling Approach
- 18. Quadrotor Modeling ConclusionsNew method for the modeling of the quadrotor’s dynamics has been proposedbased on the theory of Piecewise Affine Systems.Advantages: Captures nonlinearities and couplings of the system – Covers a larger part ofthe quadrotor’s flight envelope compared to linear approaches. Takes into account the disturbance effects of atmospheric turbulence asaffine-additive terms. Provides the opportunity to utilize Optimal/Switching control theory. Can be expanded to other rotorcrafts types.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 182. Quadrotor Modeling Approach
- 19. Quadrotor Design191. Introduction2. Quadrotor Modeling Approach3. Quadrotor Design4. Quadrotor Control Approaches5. Cooperation of UAVs6. Conclusions and Future Work
- 20. System RequirementsThe design of the UPATcopter quadrotor experimental platform should fit thefollowing requirements: The craft should be of small-size with about 0.5m diameter. The craft should not exceed 1.5Kg while also providing more than 0.5Kgadditive payload. The craft should have high-end processing capabilities. The craft should be able of complete autonomous indoor and outdoor stateestimation. The craft should have multiple wireless communication options.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras3. Quadrotor Design20
- 21. Experimental PlatformsIn the beginning of this research a Draganflyer VTi helicopter was utilized but itwas soon proved that could not fit the aforementioned requirements.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras3. Quadrotor Design21Draganflyer Vti Toy QuadrotorFirst attempt of quadrotor design
- 22. Experimental PlatformsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras3. Quadrotor Design22Second UPATcopter platformFinal UPATcopter designCarbon fiber centerplatesAnodized aluminum armsNylon/Carbon fiber propellerss
- 23. UPATcopter main hardware diagramControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 233. Quadrotor Design
- 24. Main Control UnitControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 243. Quadrotor DesignAll – in – one Single Board Computers can be utilized as Powerful, low-power, low-weight and low-cost Main Control Units.This approach provides the capability to rapidly develop and deploy control, cooperation andenvironmental perception algorithms using high-level programming methods and high-endoperation systems.picoITX was selected as the Main Control Unit of UPATcopters • 1.6GHz ATOM Z530 Processorable to cope with all requiredcontrol and perceptioncomputations• 2GB RAM• Modular connectivity throughUSB Ports, I2C Bus, SPI – Allsensors can be easily used• Easily combined with WirelessNetworks adapters• Less than 0.5A at 5V• Less than 250g with Memory andSSD Hard Disk Drive
- 25. Sensor System – Attitude/Altitude EstimationControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 253. Quadrotor DesignXsens MTi-G AttitudeHeading Reference SystemOpen-Source IMUs12oHz Maximum update rateRelatively low driftingClosed firmware100Hz Maximum update rateDriftingVery prone to vibrations30o degrees beam sonarprovides altitude data
- 26. Sensor System – Indoor horizontal motion estimationOne of the most demanding problems of complete indoor state estimation isthat of horizontal motion measurement and estimation. This problem can besolved either by using fixed cameras (higher accuracy, very high cost, notautonomous solution) or by designing onboard position estimation systems.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 263. Quadrotor Design2 Optic Flow Systems were DevelopedMouse Sensor Based Optic Flow Tam 2Micro Vision chip solutionThe Tam2 vision chip based optic flow solution implements the Image InterpolationAlgorithm (I2A) in order to derive optic flow measurements from the pixel array.I2A algorithm computes the amplitude of the translation sd between an imageregion I(n,t) captured at time t, and a later image I(n,t+Δt)
- 27. Propulsion Group Accurate control of motor-propeller control is critical in order to achieve increasedflight accuracy. DC – brushless motors were utilized due to their increased torque characteristics. The appropriate programming of the Electronic Speed Controller in high update rates(>100Hz/I2C Bus), the power consumption and the identification of the final SpeedController-Motor-Propeller system is important for the overall control problem.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 273. Quadrotor Design
- 28. Quadrotor Control Approaches281. Introduction2. Quadrotor Modeling Approach3. Quadrotor Design4. Quadrotor Control Approaches5. Cooperation of UAVs6. Conclusions and Future Work
- 29. Proposed Control Strategies A Constrained Finite Time Optimal Control (CFTOC) Strategy for the Quadrotor’sattitude set-point problem. A Switching Model Predictive Control (SMPC) Strategy for the Quadrotor’strajectory/attitude control problem. A Proportional-Integral-Derivative-2nd Derivative/ Proportional-Integral-DerivativeControl Strategy for the Quadrotor’s Attitude/Translational dynamics.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 294. Quadrotor Control Approaches
- 30. Constrained Finite Time Attitude Optimal ControlSystem Piecewise Affine Dynamics:Goals:Capture nonlinearities of the attitude subsystemAccount for state and input constraints of the systemAccount for the additive effects of wind-gust disturbancesExplicit Solution – Offline ComputationControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 304. Quadrotor Control Approaches
- 31. Constrained Finite Time Attitude Optimal ControlInput Constraints:State ConstraintsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 314. Quadrotor Control Approaches
- 32. Constrained Finite Time Attitude Optimal ControlAssuming Ts sampling periodCompute the optimal control sequence:Cost function subject to PWA dynamics:The control action is a continuous function of the following form:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 324. Quadrotor Control ApproachesConvex PolyhedronNumber of created polyhedra
- 33. Constrained Finite Time Attitude Optimal ControlExperimental studies with an initial experimental set-up consisted of a Draganflyer VTiquadrotor, Xsens MTi-G IMU and personal computer.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 334. Quadrotor Control ApproachesTait-Bryan angle rates arenot equal with p,q,r ratess
- 34. Constrained Finite Time Attitude Optimal ControlControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 344. Quadrotor Control Approaches
- 35. Constrained Finite Time Attitude Optimal ControlAttitude Regulation for 1-3-5 PWA systemsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 354. Quadrotor Control Approaches
- 36. Constrained Finite Time Attitude Optimal ControlAttitude Regulation for 1-3-5 PWA systems subject to forcible Wind-GustsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 364. Quadrotor Control Approaches
- 37. Constrained Finite Time Attitude Optimal ControlComparison of LQ (red) – CFTOC (blue)Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 374. Quadrotor Control Approaches
- 38. Constrained Finite Time Attitude Optimal ControlResponse subject to different directional wind-gustsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 384. Quadrotor Control Approaches
- 39. Constrained Finite Time Attitude Optimal ControlIn conclusion:1. CFTOC can be computed over a family of Piecewise Affine systems.2. Ensures stability among the switching.3. Accounts for the state and input constraints of the system.4. Efficient in wind-gust disturbances attenuation.5. Multi-Parametric solution has the advantage of off-line computation.6. However: excessive computational cost for systems with more than 4 statesand prediction horizon larger than 5 steps ahead– inefficient onboardimplementation. This is due to the exponential number of transitionsbetween regions which can occur when a controller is computed in adynamic programming fashion.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 394. Quadrotor Control Approaches
- 40. Switching Model Predictive ControlWhy Model Predictive Control:1. It handles multivariable control problems naturally.2. It can take into account actuator and state limitations.3. It allows operation close to constraints – more profitable operation.4. Receding horizon ‘idea’ can lead to smoother responseWhy not:1. Increased computational costs.2. Requires good knowledge of the model.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 404. Quadrotor Control ApproachesKontron pITX &good programming ==Problem Solved!Can be solved withextended simulations,CAD tools andexperimental studies.Main contributions of the proposed SMPC strategy:1. First time to design and experimentally verify a Model Predictive Control forthe quadrotor’s attitude and trajectory control problem.2. Switching control based on multiple Piecewise Affine systemrepresentations.3. Accounts for the additive effects of wind-gusts due to affine terms in themodel.4. Accounts for the state and input constraints of the quadrotor.5. Experimentally verified based on Inertial sensors, Sonar and Optic Flowposition deviation measurements. The proposed system achieves accurateposition control both in the absence and under the presence of wind-gusts,trajectory tracking and accurate attitude maneuvering.Submitted at IET Control Theory andApplications
- 41. Switching Model Predictive ControlControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 414. Quadrotor Control Approaches
- 42. Switching Model Predictive ControlDiscretized Attitude, Altitude and Horizontal Piecewise Affine Dynamics:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 424. Quadrotor Control Approaches
- 43. Switching Model Predictive ControlState and Input Constraints:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 434. Quadrotor Control ApproachesAttitude Constraints Vertical Constraints Horizontal Constraints
- 44. Switching Model Predictive ControlFor each Piecewise Affine system:with respect to the control moves and the Piecewise Affinesystem dynamics, where:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 444. Quadrotor Control ApproachesPrediction horizon (=5)Control horizon (=2)
- 45. Switching Model Predictive ControlSelected Piecewise Operation Regions and Linearization Points (Γ>>1):Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 454. Quadrotor Control Approaches
- 46. Switching Model Predictive ControlPosition Hold:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 464. Quadrotor Control Approaches
- 47. Switching Model Predictive ControlPosition Hold under Wind-Gusts:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 474. Quadrotor Control Approaches
- 48. Switching Model Predictive ControlTrajectory Control:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 484. Quadrotor Control Approaches
- 49. Switching Model Predictive ControlHover subject in the absence and under the presence of wind-gustsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 494. Quadrotor Control Approaches
- 50. Switching Model Predictive ControlAttitude ManeuverControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 504. Quadrotor Control Approaches
- 51. Switching Model Predictive ControlIn conclusion:1. Model Predictive Control is very promising for such complex multivariablesystems.2. It poses significant advantages including its abilities to account for thephysical constraints of the system and the effects of atmosphericdisturbances.3. Switching control counts for some of the nonlinearities of the system andproduces control actions for a large part of the quadrotor’s flight envelope.4. The drawback of increased computational costs can be resolved.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 514. Quadrotor Control Approaches
- 52. PIDD/PID Attitude/Position ControlProportional – Integral – Derivative – 2nd Derivative Attitude Control1. Based on classical PID theory augmented with angular accelerationfeedback.2. Extends the bandwidth of the closed-loop system – provides the opportunityto increase the control gains.3. Leads to faster tracking response.Translational dynamics are relatively slow – classical PID was utilizedThis control law was designed in order to use the computational power of theKontron pITX for other computations (Environmental Perception)Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 524. Quadrotor Control Approaches
- 53. PIDD/PID Attitude/Position ControlControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 534. Quadrotor Control Approaches
- 54. PIDD/PID Attitude/Position ControlControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 544. Quadrotor Control Approaches
- 55. PIDD/PID Attitude/Position ControlPosition Hold:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 554. Quadrotor Control Approaches
- 56. PIDD/PID Attitude/Position ControlAttitude RegulationControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 564. Quadrotor Control Approaches
- 57. PIDD/PID Attitude/Position ControlAggressive Attitude RegulationControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 574. Quadrotor Control Approaches
- 58. PIDD/PID Attitude/Position ControlPIDD/PID control:1. Combination of classical PID control schemes and angular accelerationfeedback.2. Efficient & Simple – low computational cost.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 584. Quadrotor Control Approaches
- 59. Cooperation of UnmannedAerial Vehicles591. Introduction2. Quadrotor Modeling Approach3. Quadrotor Design4. Quadrotor Control Approaches5. Cooperation of UAVs6. Conclusions and Future Work
- 60. Cooperating UAVsUAVs are utilized in more complex missions and specially ISR (Intelligence, Surveillanceand Reconnaisance). Such missions pose a number of challenging requirements:1. Environmental Perception2. Navigation under Uncertainty3. Mission Critical Issues (i.e. timing)4. RedundancyControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 605. Cooperation of UAVsCooperative UAVs
- 61. Cooperative Forest-Fire Surveillance This mission is solved using an overlapping cooperation approach. The overlapping strategy is based on simple Consensus implemented in eachUAV, while the communication between the UAVs is carried according to theconsensus algorithm:Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 615. Cooperation of UAVs
- 62. Cooperative Forest-Fire SurveillanceForest Fire propagation model required for simulation studies: Although there are complex models based on statistic data, simple geometricalmodels that assume that the fire perimeter expands like an ellipse or a folium inrelation with the wind’s blowing direction are adequate for UAV cooperationsimulation studies.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 625. Cooperation of UAVs
- 63. Cooperative Forest-Fire SurveillanceOperation goal: track every point of the evolving fire perimeter and update the locationof the fire with the least latency.Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 635. Cooperation of UAVsObjective: Equalize the paths flown by the quadrotors
- 64. Cooperative Forest-Fire SurveillanceDecentralized Solution AlgorithmControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 645. Cooperation of UAVs
- 65. Simulation Test Case:1. The fire perimeter is an ellipse and the evolution is modeled as a change on the ellipsefoci affecting its major and minor radius. The change in perimeter occurs in T=1hr,2hrsand 4hs respectively.2. Each quadrotor communicates only with the quadrotor with which they meet in arendezvous point.3. Quadrotors that have met in a rendezvous point sense fire perimeter changes. If avariation of the perimeter is sensed then both quadrotors fly to the new fireperimeter at its closed point.Cooperative Forest-Fire SurveillanceControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 655. Cooperation of UAVs
- 66. Heterogeneous UAV Swarm Area Exploration and TargetAcquisitionHeterogeneous UAV Swarm – Cooperation strategies should account for the differentvehicle capabilities, advantages and drawbacks.Assume a UAV Swarm consisted of Quadrotors with different characteristicsQuadrotors can fly forward but also hover above a target for constant surveillance: thecooperation strategy must also benefit from this capabilityControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 665. Cooperation of UAVsThe goal of the UAV-Swarm is to enter anunknown fixed dimensions area withstatic targets, explore as soon aspossible, acquire the target positions andconstantly survey them.
- 67. Heterogeneous UAV Swarm Area Exploration and TargetAcquisitionCooperation Strategy assumptions:1. The fixed dimensions unexplored area is tessellated into equal sized square Cells, Cell(k,m).2. Each UAV is equipped with two vision systems for forward look and downwards areaexploration.3. Vi is the velocity factor of the i-th UAV4. Ci is the camera factor expressing the exploration cameracapabilities of the i-th UAV1. Ei is the remaining flight endurance of the i-th UAV2. Ti is the number indicating the total allocated tasks from the i-th UAV3. Li is the number indicating the accomplish tasks from the i-th UAV4. Ai is the uncompleted allocated tasks to the i-th UAV5. Ri is the number of the maximum tasks to be executed in one round6. Mi is a binary value indicating if the i-th UAV is in hovering mode7. Xi is a binary value indicating if the i-th UAV is available for exploration (it is not when it hasacquired a target)8. fi is the front camera omnidirectional range of the i-th UAV9. cvi are the cells viewed from the exploration camera of the i-th UAV at each time10. dik,m is the distance between the i-th UAV and the center of Cell(k,m)Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 675. Cooperation of UAVs
- 68. Heterogeneous UAV Swarm Area Exploration and TargetAcquisitionControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 685. Cooperation of UAVs
- 69. Heterogeneous UAV Swarm Area Exploration and TargetAcquisitionControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 695. Cooperation of UAVs
- 70. Conclusions & Future Work701. Introduction2. Quadrotor Modeling Approach3. Quadrotor Design4. Quadrotor Control Approaches5. Cooperation of UAVs6. Conclusions and Future Work
- 71. Conclusions & Future WorkThe main contributions of this Thesis are: New Quadrotor dynamics modeling based on Piecewise Affine Systems theory Design of a powerful and modular quadrotor experimental platform capable of very complexcomputations, complete autonomous indoor state estimation and extended communicationcapabilities. Design and Experimental verification of Constrained Finite Time Optimal Controllers, SwitchingModel Predictive Control strategies and PIDD/PID control schemes. Two new UAV cooperation strategies were proposed in order to address the problems of forestfire surveillance and area exploration.Future Work:1. Design of Miniature/Micro Aerial Vehicles based on novel flying concepts: a) convertible fixed-wing to rotorcraft or inspired by nature, and b) transformerable UGV-UAV, AUV-UAV2. Design of control laws and environmental perception that would ensure precise navigationunder severe environmental disturbances by studying the nature of the aerodynamic effects3. Design and experimental verification of fully decentralized cooperation strategies for largeheterogeneous UAV swarmsControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 716. Conclusions & Future Work
- 72. Conclusions & Future WorkControl of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 726. Conclusions & Future WorkUAV WSNMicro-QuadrotorEnvironmentalPerceptionConvertible UAV: Tilt-RotorKA-GNCP-KASamara Blade
- 73. Thank you for yourattention!Thank you for yourattention!Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 73Control of Cooperative Unmanned Aerial Vehicles

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