By Haibin Duan, Pei Li
Bio-inspired Computation in Unmanned Aerial Vehicles makes a speciality of the points of course making plans, formation keep an eye on, heterogeneous cooperative keep an eye on 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 entire figuring out of control-related difficulties in UAVs, providing the newest advances in bio-inspired computation.
By combining bio-inspired computation and UAV keep watch over difficulties, key questions are explored extensive, and every piece is content-rich whereas ultimate available. With ample illustrations of simulation paintings, this ebook hyperlinks concept, algorithms and implementation tactics, demonstrating the simulation effects with photographs which are intuitive with out sacrificing educational rigor. additional, it will pay due awareness to either the conceptual framework and the implementation procedures.
The publication bargains a helpful source for scientists, researchers and graduate scholars within the box of keep an eye on, Aerospace know-how and Astronautics, in particular these drawn to synthetic intelligence and Unmanned Aerial Vehicles.
Professor Haibin Duan and Dr. Pei Li, either paintings at Beihang college (formerly Beijing collage of Aeronautics & Astronautics, BUAA). Prof Duan's educational web site is: http://hbduan.buaa.edu.cn
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Extra resources for Bio-inspired Computation in Unmanned Aerial Vehicles
To operate in complex and uncertain environments, the UAV must be able to sense and understand the environment. This capability implies that the UAV must be able to create a model of its surrounding world by conducting multisensor data fusion (MDF) and converting these data into meaningful information that supports a variety of decision-making processes. The perception system must be able to perceive and infer the state of the environment from limited information and be able to assess the intent of other agents in the environment (Sun and Duan 2012).
References Ariyur KB, Fregene KO (2008) Autonomous tracking of a ground vehicle by a UAV. In: Proceedings of American Control Conference, Seattle, WA. IEEE, pp 669–671 Beekman M, Gilchrist AL, Duncan M, Sumpter DJ (2007) What makes a honeybee scout? Behav Ecol Sociobiol 61(7):985–995 Clough BT (2002) UAV swarming? So what are those swarms, what are the implications, and how do we handle them? DTIC Document. pdf Dasgupta P (2008) A multiagent swarming system for distributed automatic target recognition using unmanned aerial vehicles.
First, a chaotic ABC approach is proposed for two-dimensional path planning. Then path planning is extended to three-dimensional scenario through an improved ACO added a path smoothing strategy in Sect. 4. In Sect. 5 coordinated path replanning for multiple UAVs is dealt with by taking advantage of Max–Min adaptive ACO. Chapter 5 deals with three significant problems in the formation flight problem, which are, respectively, formation control, close formation (tight formation), and formation configuration.
Bio-inspired Computation in Unmanned Aerial Vehicles by Haibin Duan, Pei Li