Indoor Navigation Strategies for Aerial Autonomous Systems

Indoor Navigation Strategies for Aerial Autonomous Systems presents the necessary and sufficient theoretical basis for those interested in working in unmanned aerial vehicles, providing three different approaches to mathematically represent the dynamics of an aerial vehicle.

The book contains detailed information on fusion inertial measurements for orientation stabilization and its validation in flight tests, also proposing substantial theoretical and practical validation for improving the dropped or noised signals. In addition, the book contains different strategies to control and navigate aerial systems.

The comprehensive information will be of interest to both researchers and practitioners working in automatic control, mechatronics, robotics, and UAVs, helping them improve research and motivating them to build a test-bed for future projects.

• Provides substantial information on nonlinear control approaches and their validation in flight tests
• Details in observer-delay schemes that can be applied in real-time
• Teaches how an IMU is built and how they can improve the performance of their system when applying observers or predictors
• Improves prototypes with tactics for proposed nonlinear schemes

Academics and researches in automatic control, mechatronics, robotics, Unmanned Aerial Vehicles (UAVs)

• About the Authors
• Preface
• Acknowledgments
• Part I: Background
  • Background
  • Chapter 1: State-of-the-Art
    • Abstract
    • 1.1. Mathematical Representation of the Vehicle Dynamics
    • 1.2. Attitude Estimation Using Inertial Sensors
    • 1.3. Delay Systems & Predictors
    • 1.4. Data Fusion for UAV Localization
    • 1.5. Control & Navigation Algorithms
    • 1.6. Trajectory Generation & Tracking
    • 1.7. Obstacle Avoidance
    • 1.8. Teleoperation
    • References
  • Chapter 2: Modeling Approaches
    • Abstract
    • 2.1. Force and Moment in a Rotor
    • 2.2. Euler–Lagrange Approach
    • 2.3. Newton–Euler Approach
    • 2.4. Quaternion Approach
    • 2.5. Discussion
    • References
• Part II: Improving Sensor Signals for Control Purposes
  • Improving Sensor Signals for Control Purposes
  • Chapter 3: Inertial Sensors Data Fusion for Orientation Estimation
    • Abstract
    • 3.1. Attitude Representation
    • 3.2. Sensor Characterization
    • 3.3. Attitude Estimation Algorithms
    • 3.4. A Computationally-Efficient Kalman Filter
    • 3.5. Discussion
    • References
  • Chapter 4: Delay Signals & Predictors
    • Abstract
    • 4.1. Observer–Predictor Algorithm for Compensation of Measurement Delays
    • 4.2. State Predictor–Control Scheme
    • 4.3. Discussion
    • References
  • Chapter 5: Data Fusion for UAV Localization
    • Abstract
    • 5.1. Sensor Data Fusion
    • 5.2. Prototype and Numerical Implementation
    • 5.3. Flight Tests and Experimental Results
    • 5.4. OptiTrack Measurements vs EKF Estimation
    • 5.5. Rotational Optical Flow Compensation
    • 5.6. Discussion
    • References
• Part III: Navigation Schemes & Control Strategies
  • Navigation Schemes & Control Strategies
  • Chapter 6: Nonlinear Control Algorithms with Integral Action
    • Abstract
    • 6.1. From PD to PID Controllers
    • 6.2. Saturated Controllers with Integral Component
    • 6.3. Integral and Adaptive Backstepping Control – IAB
    • 6.4. Discussion
    • References
  • Chapter 7: Sliding Mode Control
    • Abstract
    • 7.1. From the Nonlinear Attitude Representation to Linear MIMO Expression
    • 7.2. Nonlinear Optimal Controller with Integral Sliding Mode Design
    • 7.3. Numerical Validation
    • 7.4. Real-Time Validation
    • 7.5. Discussion
    • References
  • Chapter 8: Robust Simple Controllers
    • Abstract
    • 8.1. Nonlinear Robust Algorithms Based on Saturation Functions
    • 8.2. Robust Control Based on an Uncertainty Estimator
    • 8.3. Discussion
    • References
  • Chapter 9: Trajectory Generation, Planning & Tracking
    • Abstract
    • 9.1. Quadrotor Mathematical Description
    • 9.2. Time-Optimal Trajectory Generation
    • 9.3. UAV Routing Problem for Inspection-Like Missions
    • 9.4. Trajectory Tracking Problem
    • 9.5. Simulation Results
    • 9.6. Discussion
    • References
  • Chapter 10: Obstacle Avoidance
    • Abstract
    • 10.1. Artificial Potential Field Method
    • 10.2. Obstacle Avoidance Algorithm
    • 10.3. Limit-Cycle Obstacle Avoidance
    • 10.4. Discussion
    • References
  • Chapter 11: Haptic Teleoperation
    • Abstract
    • 11.1. Experimental Setup
    • 11.2. Collision Avoidance
    • 11.3. Haptic Teleoperation
    • 11.4. Real-Time Experiments
    • 11.5. Discussion
    • References
• Index