A blackboard architecture for control
Artificial Intelligence
The STATEMATE semantics of statecharts
ACM Transactions on Software Engineering and Methodology (TOSEM)
The Gaia Methodology for Agent-Oriented Analysis and Design
Autonomous Agents and Multi-Agent Systems
Specifying Rational Agents with Statecharts and Utility Functions
RoboCup 2001: Robot Soccer World Cup V
Applying Agent Oriented Software Engineering to Cooperative Robotics
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
MDA Explained: The Model Driven Architecture: Practice and Promise
MDA Explained: The Model Driven Architecture: Practice and Promise
The many faces of publish/subscribe
ACM Computing Surveys (CSUR)
Developing Intelligent Agent Systems: A Practical Guide
Developing Intelligent Agent Systems: A Practical Guide
The Agent Modeling Language (AMOLA)
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Gaia Agents Implementation through Models Transformation
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Agent-oriented modeling and development of a person-following mobile robot
Expert Systems with Applications: An International Journal
Autonomous Agents and Multi-Agent Systems
Using ASEME methodology for model-driven agent systems development
AOSE'10 Proceedings of the 11th international conference on Agent-oriented software engineering
Hi-index | 0.00 |
Modern model-driven engineering and Agent-Oriented Software Engineering (AOSE) methods are rarely utilized in developing robotic software. In this paper, we show how a Model-Driven AOSE methodology can be used for specifying the behavior of multi-robot teams. Specifically, the Agent Systems Engineering Methodology (ASEME) was used for developing the software that realizes the behavior of a physical robot team competing in the Standard Platform League of the RoboCup competition (the robot soccer world cup). The team consists of four humanoid robots, which play soccer autonomously in real time utilizing the on-board sensing, processing, and actuating capabilities, while communicating and coordinating with each other in order to achieve their common goal of winning the game. Our work focuses on the challenges of coordinating the base functionalities (object recognition, localization, motion skills) within each robot (intra-agent control) and coordinating the activities of the robots towards a desired team behavior (inter-agent control). We discuss the difficulties we faced and present the solutions we gave to a number of practical issues, which, in our view, are inherent in applying any AOSE methodology to robotics. We demonstrate the added value of using an AOSE methodology in the development of robotic systems, as ASEME allowed for a platform-independent team behavior specification, automated a large part of the code generation process, and reduced the total development time.