Cooperative Path Planning of Unmanned Aerial Vehicles

ISBN-13: 9780470741290 / Angielski / Twarda / 2011 / 214 str.

Antonios Tsourdos; Brian White; Madhavan Shanmugavel

Opis

An invaluable addition to the literature on UAV guidance and cooperative control, Cooperative Path Planning of Unmanned Aerial Vehicles is a dedicated, practical guide to computational path planning for UAVs. One of the key issues facing future development of UAVs is path planning: it is vital that swarm UAVs/ MAVs can cooperate together in a coordinated manner, obeying a pre-planned course but able to react to their environment by communicating and cooperating. An optimized path is necessary in order to ensure a UAV completes its mission efficiently, safely, and successfully.

Focussing on the path planning of multiple UAVs for simultaneous arrival on target, Cooperative Path Planning of Unmanned Aerial Vehicles also offers coverage of path planners that are applicable to land, sea, or space-borne vehicles. Cooperative Path Planning of Unmanned Aerial Vehicles is authored by leading researchers from Cranfield University and provides an authoritative resource for researchers, academics and engineers working in the area of cooperative systems, cooperative control and optimization particularly in the aerospace industry.

Spis treści

About the Authors.

Series Preface.

Preface.

Acknowledgements.

List of Figures.

List of Tables.

Nomenclature.

1. Introduction.

1.1 Path Planning Formulation.

1.2 Path Planning Constraints.

1.3 Cooperative Path Planning and Mission Planning.

1.4 Path Planning An Overview.

1.5 The Road Map Method.

1.6 Probabilistic Methods.

1.7 Potential Field.

1.8 Cell Decomposition.

1.9 Optimal Control.

1.10 Optimization Techniques.

1.11 Trajectories for Path Planning.

1.12 Outline of the Book.

References.

2. Path Planning in Two Dimensions.

2.1 Dubins Paths.

2.2 Designing Dubins Path using Analytical Geometry.

2.3 Existence of Dubins Paths.

2.4 Length of Dubins Paths.

2.5 Design of Dubins Paths using Principles of Differential Geometry.

2.6 Path of Continuous Curvature.

2.7 Producing Flyable Clothoid Paths.

28 Producing Flyable Pythagorean Hodograph Paths (2D).

References.

3. Path Planning in Three Dimensions.

3.1 Dubins Paths in Three Dimensions Using Differential Geometry.

3.2 Path Length Dubins 3D.

3.3 Pythagorean Hodograph Paths 3D.

3.4 Design of Flyable Paths Using PH Curves.

References.

4. Collision Avoidance.

4.1 Research into Obstacle Avoidance.

4.2 Obstacle Avoidance for Mapped Obstacles.

4.3 Obstacle Avoidance of Unmapped Static Obstacles.

4.4 Algorithmic Implementation.

References.

5. Path–Following Guidance.

5.1 Path Following the Dubins Path.

5.2 Linear Guidance Algorithm.

5.3 Nonlinear Dynamic Inversion Guidance.

5.4 Dynamic Obstacle Avoidance Guidance.

References.

6. Path Planning for Multiple UAVs.

6.1 Problem Formulation.

6.2 Simultaneous Arrival.

6.3 Phase I: Producing Flyable Paths.

6.4 Phase II: Producing Feasible Paths.

6.5 Phase III: Equalizing Path Length.

6.6 Multiple Path Algorithm.

6.7 Algorithm Application for Multiple UAVs.

6.8 2D Pythagorean Hodograph Paths.

6.9 3D Dubins Paths.

6.10 3D Pythagorean Hodograph Paths.

References.

Appendix A Differential Geometry.

Appendix B. Pythagorean Hodograph.

Index. 

O autorze

Antonios Tsourdos is a Reader in Autonomous Systems and Control and Head of the Guidance and Control Group at Cranfield. His research areas include UAV Autonomy, UAV Path Planning, Coordinated Guidance, Cooperative Control, UAV Swarm, Autonomous Sensors Network, Sensor and Data Fusion, and Vehicle Health Management. He has authored many scientific research papers and has served as a guest editor for journal special issues on ′multi–vehicle systems cooperative control with applications′; ′advances in missile guidance and control: theory and practice′, and cooperative control approaches for multiple mobile robots′.

Brian A White, now Professor Emeritus at Cranfield, was until recently Head of the Department of Aerospace, Power and Sensors and also Head of the Guidance and Control Group at Cranfield. His areas of expertise are robust control, non–linear control, estimation, observer applications, inertial navigation, guidance design, soft computing and sensor and data fusion. He has published widely in the control science field, mainly on autopilot design and guidance. He has managed significant contracts in the area of guidance. He has organized and run numerous invited sessions at major control conferences and co–edited a special issue of the IFAC journal Control Engineering Practice on Control in Defence Systems. He has served as associate editor for the IMechE Journal of Aerospace Engineering (Part G), IMechE Journal of Systems and Control Engineering (Part I), and the Journal of Nonlinear Studies.

Madhavan Shanmugavel is a Research Officer within the Guidance and Control Group at Cranfield.

Blurb

An invaluable addition to the literature on UAV guidance and cooperative control, Cooperative Path Planning of Unmanned Aerial Vehicles is a dedicated, practical guide to computational path planning for UAVs. One of the key issues facing future development of UAVs is path planning: it is vital that swarm UAVs/ MAVs can cooperate together in a coordinated manner, obeying a pre–planned course but able to react to their environment by communicating and cooperating. An optimized path is necessary in order to ensure a UAV completes its mission efficiently, safely, and successfully.

Focussing on the path planning of multiple UAVs for simultaneous arrival on target, Cooperative Path Planning of Unmanned Aerial Vehicles also offers coverage of path planners that are applicable to land, sea, or space–borne vehicles.

Cooperative Path Planning of Unmanned Aerial Vehicles is authored by leading researchers from Cranfield University and provides an authoritative resource for researchers, academics and engineers working in the area of cooperative systems, cooperative control and optimization particularly in the aerospace industry.

• Include chapters on path planning, 3–D path planning, cooperative path planning, path planning in complex environments as well as guidance for accurate path following and sense and avoid algorithms to deal with collision avoidance
• Approaches the solution to UAV path planning via two phases: producing paths to meet curvature constraints – the flyable paths, and then tuning the flyable paths to meet the mission demands
• Describes flyable path approaches using composite curves using Dubins and Clothoid principles, and continuous curves using Pythagorean Hodograph principles; and extends these approaches to cater for the complex problem of obstacle avoidance.