Antonios Tsourdos

Bio: Antonios Tsourdos is an academic researcher from Cranfield University. The author has contributed to research in topics: Missile & Computer science. The author has an hindex of 34, co-authored 542 publications receiving 5244 citations. Previous affiliations of Antonios Tsourdos include Royal Military College of Canada & Defence Academy of the United Kingdom.

Cooperative Path Planning of Unmanned Aerial Vehicles

Abstract: 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.

Nonlinear Model Predictive Coordinated Standoff Tracking of a Moving Ground Vehicle

Abstract: This paper proposes a nonlinear model-predictive control framework for coordinated standoff tracking by a pair of unmanned aerial vehicles. The benefit of this approach is to get optimal performance compared with using a decoupled controller structure: heading control for standoff-distance keeping and speed control for phase keeping. The overall controller structure is fully decentralized as each unmanned aerial vehicle optimizes its controller based solely on the future propagation of the pair vehicle states and the target estimates received via communication. This paper uses an acceleration model for sophisticated and realistic target dynamics, which can consider a more reasonable system noise covariance matrix reflecting the target’s motion characteristics. To simplify optimization formulation and decrease computation burden, a new manipulation using the inner product of position vectors of the unmanned aerial vehicles with respect to the target position is proposed for antipodal tracking instead of us...

Collision Avoidance Strategies for Unmanned Aerial Vehicles in Formation Flight

Abstract: Collision avoidance strategies for multiple unmanned aerial vehicles (UAVs) based on geometry are investigated in this study. The proposed strategies allow a group of UAVs to avoid obstacles and separate if necessary through a simple algorithm with low computation by expanding the collision-cone approach to formation of UAVs. The geometric approach uses line-of-sight vectors and relative velocity vectors where dynamic constraints are included in the formation. Each UAV can determine which plane and direction are available for collision avoidance. An analysis is performed to define an envelope for collision avoidance, where angular rate limits and obstacle detection range limits are considered. Based on the collision avoidance envelope, each UAV in a formation determines whether the formation can be maintained or not while avoiding obstacles. Numerical simulations are performed to demonstrate the performance of the proposed strategies.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Evolution and the Theory of Games

Abstract: In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.