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Behavior-based model predictive control for networked multi-agent systems

We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

Identiferoai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/51864
Date22 May 2014
CreatorsDroge, Greg Nathanael
ContributorsEgerstedt, Magnus
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Languageen_US
Detected LanguageEnglish
TypeDissertation
Formatapplication/pdf

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