DISTRIBUTED CONSENSUS IN MULTIVEHICLE COOPERATIVE CONTROL PDF

The final prices may differ from the prices shown due to specifics of VAT rules About this book The coordinated use of autonomous vehicles has an abundance of potential applications from the domestic to the hazardously toxic. Frequently the communications necessary for the productive interplay of such vehicles may be subject to limitations in range, bandwidth, noise and other causes of unreliability. Information consensus guarantees that vehicles sharing information over a network topology have a consistent view of information critical to the coordination task. Assuming only neighbor-neighbor interaction between vehicles, Distributed Consensus in Multi-vehicle Cooperative Control develops distributed consensus strategies designed to ensure that the information states of all vehicles in a network converge to a common value.

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Resources and Help Distributed Consensus Algorithms and Their Applications in Multi-vehicle Cooperative Control Abstract: While autonomous vehicles that perform solo missions can yield significant benefits, greater efficiency and operational capability will be realized from teams of autonomous vehicles operating in a coordinated fashion.

Potential applications for multiple autonomous vehicles include autonomous household appliances, hazardous material handling systems, distributed reconfigurable sensor networks, surveillance and reconnaissance, space-based interferometry, and future autonomous combat systems.

To enable these applications, a variety of cooperative control capabilities need to be developed. These capabilities include formation control, rendezvous, attitude alignment, flocking, foraging, task and role assignment, payload transport, air traffic control, and cooperative search. Execution of these capabilities requires that individual vehicles share a consistent view of the objectives and the world.

Information consensus guarantees that vehicles sharing information over a network topology have a consistent view of information that is critical to the coordination task. By necessity, consensus algorithms are designed to be distributed, assuming only neighbor-to-neighbor interaction between vehicles.

Consensus algorithms have applications in rendezvous, formation control, flocking, attitude alignment, and sensor networks. The purpose of this workshop is overview the research of distributed consensus algorithms in cooperative control of multiple autonomous vehicles.

The presenter has been actively involved in this area over the past several years. In particular, theoretical aspects of distributed consensus algorithms will be introduced. Application examples of distributed consensus algorithms in multi-vehicle cooperative control will also be presented.

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Distributed Consensus in Multi-vehicle Cooperative Control

About this book Introduction The coordinated use of autonomous vehicles has an abundance of potential applications from the domestic to the hazardously toxic. Frequently the communications necessary for the productive interplay of such vehicles may be subject to limitations in range, bandwidth, noise and other causes of unreliability. Information consensus guarantees that vehicles sharing information over a network topology have a consistent view of information critical to the coordination task. Assuming only neighbor-neighbor interaction between vehicles, Distributed Consensus in Multi-vehicle Cooperative Control develops distributed consensus strategies designed to ensure that the information states of all vehicles in a network converge to a common value. This approach strengthens the team, minimizing power consumption and the deleterious effects of range and other restrictions. Notation drawn from graph and matrix theory and background material on linear and nonlinear system theory are enumerated in six appendices.

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Resources and Help Distributed Consensus Algorithms and Their Applications in Multi-vehicle Cooperative Control Abstract: While autonomous vehicles that perform solo missions can yield significant benefits, greater efficiency and operational capability will be realized from teams of autonomous vehicles operating in a coordinated fashion. Potential applications for multiple autonomous vehicles include autonomous household appliances, hazardous material handling systems, distributed reconfigurable sensor networks, surveillance and reconnaissance, space-based interferometry, and future autonomous combat systems. To enable these applications, a variety of cooperative control capabilities need to be developed. These capabilities include formation control, rendezvous, attitude alignment, flocking, foraging, task and role assignment, payload transport, air traffic control, and cooperative search. Execution of these capabilities requires that individual vehicles share a consistent view of the objectives and the world.

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