By Haibin Duan, Pei Li
Bio-inspired Computation in Unmanned Aerial Vehicles makes a speciality of the elements of direction making plans, formation keep watch over, heterogeneous cooperative keep watch over and vision-based surveillance and navigation in Unmanned Aerial autos (UAVs) from the point of view of bio-inspired computation. It is helping readers to achieve a accomplished realizing of control-related difficulties in UAVs, offering the newest advances in bio-inspired computation.
By combining bio-inspired computation and UAV regulate difficulties, key questions are explored extensive, and every piece is content-rich whereas ultimate obtainable. With plentiful illustrations of simulation paintings, this publication hyperlinks idea, algorithms and implementation techniques, demonstrating the simulation effects with photographs which are intuitive with no sacrificing educational rigor. extra, it will pay due consciousness to either the conceptual framework and the implementation procedures.
The ebook bargains a invaluable source for scientists, researchers and graduate scholars within the box of keep an eye on, Aerospace expertise and Astronautics, in particular these attracted to man made intelligence and Unmanned Aerial Vehicles.
Professor Haibin Duan and Dr. Pei Li, either paintings at Beihang collage (formerly Beijing college of Aeronautics & Astronautics, BUAA). Prof Duan's educational web site is: http://hbduan.buaa.edu.cn
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Additional info for Bio-inspired Computation in Unmanned Aerial Vehicles
2 Enhance Robustness and Flexibility We have seen that complex colony-level structures and many aspects of the socalled swarm intelligence of social insects can be understood in terms of interaction networks and feedback loops among individuals. These are the basic elements that allow the emergence of dynamic patterns at the colony level. These patterns can be material or social and lead the colony to structure its environment and solve problems. The most interesting properties of these self-organized patterns are robustness (the ability for a system to perform without failure under a wide range of conditions) and flexibility (the ability for a system to readily adapt to new, different, or changing requirements).
These interactions promote the positive feedbacks that create the collective structures and act for their subsistence against negative feedbacks that tend to eliminate them. Emergent properties: They display properties that are more complex than the simple contribution of each agent. These properties arise from the nonlinear combination of the interactions between the members of the colony. Bifurcations: A bifurcation is the appearance of new stable solutions when some of the system’s parameters change.
In this chapter, we focus on four popular bio-inspired optimization algorithms, which are, respectively, ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). 2 Ant Colony Optimization ACO is a metaheuristic for solving hard combinatorial optimization problems (Duan 2005, 2010; Duan et al. 2011). The inspiring source of ACO is the pheromone trail laying and following behavior of real ants, which use pheromones as a communication medium (Fig.