MAES: A Multi-Agent Systems Framework for Embedded Systems

Master thesis (2017)

Authors

C. Chan Zheng Electrical Engineering, Mathematics and Computer Science

Contributors

K.G. Langendoen (mentor)
J. Carvajal Godínez (graduation committee member)
A. Menicucci (graduation committee member)
M.T.J. Spaan (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2017 Carmen Chan Zheng
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Published Date

29-09-2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Miniaturization and cost reduction of hardware components have created a trend in the space industry where the traditional centralized computer is being replaced by distributed computer architecture. However, this trend comes with a cost: the on-board software complexity of the space missions has increased. The complexity has origins in the requirements of the missions where in general, these are coordination and control-related processes. As the coordination and the control of the satellite's activities are not trivial tasks, the Multi-Agent Systems(MAS)-approach has been proposed as a new architectural style due to its distributed nature. There are several existing frameworks for implementing MAS-based applications, however, most of them are neither designed to satisfy real-time requirements nor designed to be implemented in highly-constrained embedded systems. Therefore, the purpose of this thesis is to develop a new tool for MAS-based applications: A Multi-Agent Framework for Embedded Systems (MAES).

The framework was implemented on top of a Real-Time Operating System: TI-RTOS, therefore, applications implemented with MAES have realtime characteristics. Experiments have shown that the execution time of an Attitude Determination algorithm is consistent on each call with a variance value of the order of 10^5 [s^2], demonstrating the predictability of the framework. Furthermore, the user coding effort is reduced as several routines are standardized and encapsulated into MAES' API. However, the predictability and ease-of-use come with a slight cost: experiments have shown that MAES-based applications lead to an increase of 6.7 KB in average in Flash memory and 4.5 KB in average in SRAM memory with respect to its non-agent implementation. Also, the CPU utilization increases as inter-agent communication requires additional processing time, also increasing the power consumption. However, the increase is low as the results have shown that is less than 1% in average.

Files

Thesis.pdf
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