Selected papers from the 2nd International Symposium on UAVs, Reno, Nevada, U.S.A. June 8–10, 2009

Cover......Page 1
Selected papers from the\r2nd International Symposium\ron UAVs, Reno, Nevada, U.S.A.\rJune 8Y10, 2009......Page 4
ISBN 9789048187638......Page 5
Table of Contents\r......Page 6
From the Editor-in-Chief......Page 9
JINT EDITORIAL BOARD-EFFECTIVE JANUARY 2010......Page 11
Accurate Modeling and Robust Hovering Control for a Quad–rotor VTOL Aircraft......Page 17
1 Introduction......Page 18
2 Dynamic Equations of Motion......Page 20
2.1 Quasi-Lagrange Equations of Motion......Page 21
2.2 Earth-Fixed Vector Representation......Page 22
2.3 Equations of Motion of a QRT UAV......Page 23
3 Controller Design......Page 24
3.1 Basic Controller......Page 26
3.2 Disturbance Observer Based Disturbance Compensation......Page 29
4.1 Experimental Setup......Page 30
4.3 Experiments and Results......Page 31
References......Page 33
1 Introduction......Page 35
2 The muFly Helicopter......Page 36
3.1 Coordinate Frames, Kinematics and Dynamics......Page 37
3.2 Forces and Moments......Page 39
3.3 Stabilizer Bar and Swash Plate......Page 41
3.4 Electro Motor......Page 43
4.2 Aerodynamical Parameters......Page 44
4.4 Prediction Error Method......Page 45
4.5 Data Generation......Page 49
5 Results......Page 50
Appendix......Page 51
References......Page 54
1 Introduction......Page 57
2.2 Visual Sensors......Page 59
2.2.1 Position......Page 60
2.2.2 Optical Flow (OF)......Page 61
2.3 Kalman-based Sensor Fusion......Page 63
3 Dynamical Model......Page 65
4 Control Strategy......Page 67
4.2 Lateral Subsystem (Scamx )......Page 68
5.1 Experimental Results......Page 69
References......Page 71
A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance......Page 73
1 Introduction......Page 74
2.2 Previous Surveys......Page 75
2.3.1 Problem Space......Page 76
2.4.1 Algorithmic Complexity......Page 78
2.5.2 Point Vehicle with Differential Constraints......Page 79
2.5.7 General Vehicle with Differential Constraints......Page 80
2.7 Algorithm Performance Criteria......Page 81
3 Recent Work in Path Planning Without Differential Constraints......Page 83
3.1.4 Freeway Method......Page 86
3.2.4 Connected Balls in Free Space......Page 87
3.5 Potential Field Methods......Page 88
3.5.3 Harmonic Potential Functions......Page 89
3.6.2 Potential Field with a Global Optimization Search or Learning Algorithm......Page 90
4 Trajectory Planning with Differential Constraints......Page 91
4.1.2 State-Space Navigation Function with Interpolation......Page 92
4.2.1 Minimum Distance Discrete Path Followed by Trajectory Forming (Two-Step Approach)......Page 94
4.2.3 Hierarchical Decoupled Planning and Control......Page 95
4.2.6 2-D Voronoi Solutions from Multiple Body-Based Planes......Page 96
4.3 Finite-State Motion Model: The Maneuver Automaton (MA)......Page 97
4.5 Receding Horizon Control (Model Predictive Control)......Page 98
4.6.3 Planning in the Presence of Uncertainties......Page 100
4.6.4 Reactive Planning......Page 101
5 Discussion......Page 102
References......Page 103
An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments......Page 109
1 Introduction......Page 110
2.1 RRT Path Planning......Page 111
2.2 Path Smoothing......Page 112
3.1 Helicopter Model......Page 113
3.2 PID Control......Page 114
3.3 Model Predictive Control......Page 115
3.4 Path Following Simulations......Page 116
4.1 Decreasing Curvature Path Generation......Page 118
5 Simulation Experiments......Page 124
5.1 Fully Known Environment......Page 125
5.2 Partially Known Environment......Page 126
5.3 Totally Unknown Environment......Page 127
References......Page 129
On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles......Page 131
1 Introduction......Page 132
2 Related Works......Page 133
3.1 Problem Statement......Page 134
3.2 Rapidly-Exploring Random Tree......Page 136
3.3 Pythagorean Hodograph Curves......Page 137
3.4 Realizable Path Calculation......Page 138
3.5 Single Trajectory Planning......Page 140
3.6 Trajectory Replanning......Page 142
3.7 Multiple Trajectory Planning......Page 143
4 Experiments......Page 144
5 Conclusion and Future Works......Page 148
References......Page 149
Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs......Page 151
1 Introduction......Page 152
2 Dynamically Feasible Path Planning Algorithm......Page 154
2.1 First Step: Connectivity Path......Page 156
2.2 Second Step: Dynamically Feasible B-Spline Algorithm......Page 158
3 Maneuver Planning Algorithm......Page 163
3.1 Brief Review of Multi Modal Control Framework......Page 164
3.2 Maneuver Generation Algorithm......Page 166
3.2.2 Selection of Modal Inputs......Page 167
3.2.3 Satisfying the Sequential Constraints......Page 170
3.2.4 Recovering the Feasible Modal Sequence......Page 171
4 Simulation Results......Page 173
5 Conclusion......Page 175
References......Page 176
1 Introduction......Page 179
4 Paper Outline......Page 181
5 Related Work......Page 182
6 Challenges of Implementing Vision for a VTOL......Page 183
7 Tracking System......Page 184
7.2 Target Selection Module......Page 185
7.3 Matching......Page 186
7.4 Template Update Module......Page 187
7.4.1 Evaluating the Template Update Module......Page 188
7.5 Pan–Tilt Controller......Page 189
9 Description of the System......Page 191
10 Conclusion......Page 196
11 Discussion......Page 197
References......Page 198
Vision-Based Road-Following Using Proportional Navigation......Page 201
1 Introduction......Page 202
2 Navigation and Camera Models......Page 204
3.1 Image-Processing Algorithm......Page 207
3.2 Skid-to-Turn Model with Strap-Down Camera......Page 209
3.3 Bank-to-Turn Model with Gimbaled Camera......Page 214
4 Image Steady-State Error......Page 215
5.1 Hardware......Page 217
5.2 Software......Page 218
6.1 Simulation Results......Page 219
6.2 Flight Test Results......Page 221
7 Conclusions......Page 223
References......Page 224
1 Introduction......Page 225
2.1 Airframe and Avionics......Page 227
2.2 Landing Equipment......Page 229
3.1 Color-Based Target Detection......Page 230
3.2 Moment-Based Target Detection......Page 231
3.3 Visual Servoing......Page 235
5 Conclusion......Page 238
References......Page 239
A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks......Page 241
1 Introduction......Page 242
2 Test-bed and Experimental Task......Page 244
3 Vision Based Approach......Page 245
3.1 Feature Detection for Natural Landmark Tracking......Page 246
3.2 Optical Flow for Safe Landing Area Classification......Page 248
4 Control Strategy......Page 250
4.1 Switching Control Strategy by Behaviour......Page 252
4.2 Ground Effect Estimation during Low Height Flights and Landing Flights......Page 253
5.1 Simulation of Vision Sensor Feedback in a Virtual Scenario......Page 255
5.2 Simulation of Ground Effect during Low Height Flights and Landing Flights......Page 257
5.3 Vision Results in Real Scenario......Page 258
6 Conclusions and Future Works......Page 262
References......Page 264
Autonomous Vision-Based Helicopter Flights Through Obstacle Gates......Page 267
1 Introduction......Page 268
2 Flight Hardware......Page 269
3.1 Image Processing......Page 270
3.2 Estimation of the Gate Position......Page 272
4.1 Preliminary Transformations......Page 273
4.2 Improvement with Multiple Measurements......Page 274
4.3 Trajectory Fitting......Page 275
4.4 Target Fitting......Page 278
5 Flight Mission Execution......Page 279
6.1 Preliminary Manual Flights......Page 280
6.2 Autonomous Flights......Page 283
7 Conclusion and Future Work......Page 286
References......Page 287
1 Introduction......Page 289
2 Algorithms......Page 292
2.1 Mapping......Page 293
2.2 Safe Landing Zone ID......Page 294
3.1 SISTR......Page 297
3.3 Obscurant Characterization......Page 298
4 Platform......Page 300
5 Experimental Results......Page 301
6 Conclusions and Future Work......Page 302
References......Page 303
1 Introduction......Page 305
2 Related Work......Page 306
3 The Characteristics of the Wii Remote Infrared Camera......Page 307
4 The UAV System......Page 308
5.1 Pattern Analysis......Page 309
5.2 Estimating Yaw and Distance......Page 310
5.3 Estimating the x and y Position......Page 311
6.1 Tracking and Flight Control Method......Page 312
6.2 Height Controller......Page 313
7.1 Attitude Estimation......Page 314
7.2 Flight Control Accuracy......Page 315
8 Conclusion and Future Work......Page 317
References......Page 318
Landing and Perching on Vertical Surfaces with Microspines for Small Unmanned Air Vehicles......Page 321
2.1 Landing and Perching Maneuvers......Page 322
3 Vertical Perching Strategy......Page 323
4.1 Microspine Requirements for Landing......Page 324
4.2 Preventing Vertical Rebound......Page 326
4.2.1 Spring, Damper and Coulomb Friction......Page 327
4.3 Non-Linear Damping......Page 328
4.4 Suspension Testing......Page 329
5 Airplane Trajectory......Page 330
6 Sensors and Control......Page 332
7 Results and Future Work......Page 333
8 Conclusions and Future Work......Page 334
References......Page 335
Automating Human Thought Processes for a UAV Forced Landing......Page 337
1 Introduction......Page 338
2 Path Planning, Guidance and Control......Page 339
2.1 Path Planning......Page 341
2.2 Guidance and Control......Page 345
2.3 Results and Discussion......Page 349
3 Multi-criteria Decision Making......Page 352
3.2 Multiple Objectives......Page 353
3.4 Decision Making Methods......Page 354
4 Conclusions......Page 355
References......Page 356
Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control......Page 359
1 Introduction......Page 360
2 History and State of the Art of Autonomous Autorotation......Page 361
3 Proposed Approach......Page 362
3.1 Vertical Autorotation Model......Page 363
3.2 Simulation Model......Page 365
4 Controller Derivation......Page 366
5.1 Baseline Scenario......Page 368
5.4 Execution Speed......Page 371
6 Conclusions and Future Work......Page 375
References......Page 376
Multimodal Interface Technologies for UAV Ground Control Stations......Page 379
1 Introduction......Page 380
2.1.1 3D Audio......Page 381
2.1.3 Haptic Devices......Page 382
2.3.1 2DoF Head Tracking......Page 383
2.3.4 Body Motion Sensors......Page 384
3.1 System Description......Page 385
3.2 Tests Performed and Results......Page 387
3.2.2 Experiment #2: Touch Screen Interface......Page 388
3.2.3 Experiment #3: Speech Synthesis......Page 389
3.2.5 Experiment #5: Tactile Interfaces......Page 390
3.2.6 Experiment #6: Integrated 3D Audio and Tactile Interfaces......Page 391
4 Analysis of the Results......Page 393
4.1 Probability Density Functions......Page 394
4.2 Comparative Results among Technologies......Page 396
5 Conclusions and Future Developments......Page 397
References......Page 398
Multi-UAV Simulator Utilizing X-Plane......Page 401
2 Simulator Hardware......Page 402
3.1 X-Plane Simulator......Page 404
3.2 Control Software......Page 406
4.1 Formation Controller......Page 407
4.2 Vehicle Controller......Page 409
5.1 Hovering Formation......Page 410
5.2 Multi-UAV Flocking......Page 411
References......Page 414
1 Introduction......Page 415
2 Overview......Page 416
3 Physics......Page 418
4.1 Autopilot......Page 419
4.4 Hardware in the Loop......Page 420
5.2 Aviones......Page 421
6 Flight Tests......Page 422
References......Page 423
Multi-UAV Cooperation and Control for Load Transportation and Deployment......Page 425
1 Introduction......Page 426
2 Architecture for Aerial Robots Cooperation in the AWARE Platform......Page 428
2.1 Architecture......Page 429
2.2 Task Model......Page 430
2.3 Task and Synchronization Managers......Page 433
2.4.1 Plan Builder / Optimizer: Offline Planning......Page 434
2.4.2 Plan Builder/Optimizer: Online Planning......Page 436
2.5 CNP Manager......Page 437
2.5.1 Dynamic Single Task Negotiations with Multiple Allocations to a Single Robot (SIT)......Page 438
2.6 Plan Merging Module......Page 440
2.7 Plan Refining Toolbox......Page 441
3 Multi-UAV Load Deployment......Page 442
3.1 Modeling......Page 443
3.2 Controller Design......Page 445
3.3 System Description......Page 448
3.4 Experimental Results......Page 449
4 Multi-UAV Deployment of Multiple Loads: Experimental Results......Page 451
4.1 Mission Description......Page 452
4.2 ODL Modules During the Mission......Page 453
5 Conclusions......Page 455
References......Page 456
1 Introduction......Page 459
2 Related Work......Page 460
3 System Components......Page 461
4 Localisation Process......Page 462
4.1.1 Grid Greyscale Histogram......Page 463
4.1.2 Weighted Grid Orientation Histogram......Page 464
4.1.3 Weighted Grid Integral Invariants......Page 465
4.2 Localisation Phase......Page 466
5.1 Datasets......Page 467
5.2.1 Comparison of Similarity Measures......Page 468
5.2.2 Comparison of Feature Extraction Algorithms......Page 470
References......Page 471
A Rotary-wing Unmanned Air Vehicle for Aquatic Weed Surveillance and Management......Page 475
2 Related Works......Page 476
3.1 Alligator Weed......Page 477
4 Aquatic Weed Surveillance and the Management Process Flow......Page 478
5 Architecture......Page 480
5.1.2 Image Acquisition Subsystem......Page 481
5.1.4 Communication Subsystem......Page 482
5.2 The RUAV Platform......Page 483
6 Field Trials......Page 485
7.1 Support Vector Machine......Page 486
9 Conclusion and Future Works......Page 490
References......Page 491
Development and Evaluation of a Chase View for UAV Operations in Cluttered Environments......Page 493
1 Introduction......Page 494
2 Previous Work......Page 496
3 Methods Towards Generating Chase Viewpoint......Page 497
3.1.1 Feature Detection and Tracking......Page 498
3.1.2 Reconstruction and Mapping......Page 499
4 Experiment Setup......Page 503
4.2 Software......Page 504
4.3 User Interface......Page 506
4.4 Procedure......Page 507
5 Results and Discussion......Page 508
References......Page 510
1 Introduction......Page 513
2 Trade-off Analysis......Page 516
3.1.1 Propeller Propulsion System......Page 517
3.2 Scaled Specific Thrust; A Quantitative Approach......Page 518
4.1.1 Component Based Constant Weight Inputs......Page 521
4.2.1 Empty Weight Calculation......Page 522
4.2.2 Battery Weight Calculation......Page 526
4.2.3 Drag Force Modeling......Page 528
4.3.2 Requirements, Variables and Constraints of the Optimization Problem......Page 530
4.3.3 Mathematical Formulation of the Problem and Objective Function......Page 531
4.3.4 Optimization Methodology......Page 532
6 Conclusions......Page 535
References......Page 536