Cooperative Path Planning of Unmanned Aerial Vehicles
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.
List of Figures.
List of Tables.
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.
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).
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.
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.
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.
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.
Appendix A Differential Geometry.
Appendix B. Pythagorean Hodograph.
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.
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