Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand

In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignmen

Platform-level Distributed Warfare Model-based on Multi-Agent System Framework

The multi-agent paradigm has become a useful tool in solving military problems. However, one of key challenges in multi-agent model for distributed warfare could be how to describe the microcosmic  tactical warfare platforms actions. In this paper, a platform-level distributed warfare model based on multi-agent system framework is designed to tackle this challenge. The basic ideas include:  Establishing multi-agent model by mapping from tactical warfare system’s members, i.e., warfare platforms, to respective agents; performing task decomposition and task allocation by using task-tree decomposition method and improved contract net protocol model technique; and implementing simulation by presenting battlefield terrain environment analysis algorithm based on grid approach. The  simulation demonstration results show that our model provides a feasible and effective approach to supporting the abstraction and representation of microcosmic tactical actions for complex warfare system.Defence Science Journal, 2012, 62(1), pp.180-186, DOI:http://dx.doi.org/10.14429/dsj.62.96

A Framework for Developing Agent-Based Distributed Applications

Abstract—The development of large-scale distributed multiagent systems in open dynamic environments is a challenge. System behavior is often not predictable and can only be evaluated by execution. This paper proposes a framework to support design and development of such systems: a framework in which both simulation and emulation play an important role. A distributed agent platform (AgentScape) is used to illustrate the potential of the framework. Keywords-multi-agent systems, agent-based simulation, emulation, development, distributed systems I

Modeling and Simulating Distributed Industrial Systems - A Multi-Agent Methodological Approach

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.60.2430&rep=rep1&type=pdfDraftWe are located in the context of the industrial system simulation, which are complex and distributed in operational, informational and decisional terms. In this chapter, we present the problems and a methodological solution. This methodology is based on the systemic approach and on multi-agent systems. It allows the modelling of distributed industrial systems such as enterprise consortiums. Moreover, it proposes a software platform architecture whish is currently instanced with Arena and dedicated agents

Collaborative, Distributed Simulations of Agri-Food Supply Chains. Analysis on How Linking Theory and Practice by Using Multi-agent Structures

Simulations help to understand and predict the behaviour of complex phenomena’s, likewise distributed socio-technical systems or how stakeholders interacts in complex domains. Such domains are normally based on networked based interaction, where information, product and decision flows comes in to play, especially under the well-known supply chains structures. Although tools exist to simulate supply chains, they do not adequately support multiple stakeholders to collaboratively create and explore a variety of decision-making scenarios. Hence, in order to provide a preliminary understanding on how these interaction affects stakeholders decision-making, this research presents an study, analysis and proposal development of robust platform to collaboratively build and simulate communication among supply chain. Since realistic supply chain behaviours are complex, a multi-agent approach was selected in order to represent such complexities in a standardised manner. The platform provides agent behaviours for common agent patterns. It provides extension hotspots to implement more specific agent behaviour for expert users (that requires programming). Therefore, as key contribution, technical aspects of the platform are presented, and also the role of multi-level supply chain scenario simulation is discussed and analysed, especially under de context of digital supply chain transformation in the agri-food context. Finally, we discuss lessons learned from early tests with the reference implementation of the platform

Programmability and Performance of Parallel ECS-based Simulation of Multi-Agent Exploration Models

While the traditional objective of parallel/distributed simulation techniques has been mainly in improving performance and making very large models tractable, more recent research trends targeted complementary aspects, such as the “ease of programming”. Along this line, a recent proposal called Event and Cross State (ECS) synchronization, stands as a solution allowing to break the traditional programming rules proper of Parallel Discrete Event Simulation (PDES) systems, where the application code processing a specific event is only allowed to access the state (namely the memory image) of the target simulation object. In fact with ECS, the programmer is allowed to write ANSI-C event-handlers capable of accessing (in either read or write mode) the state of whichever simulation object included in the simulation model. Correct concurrent execution of events, e.g., on top of multi-core machines, is guaranteed by ECS with no intervention by the programmer, who is in practice exposed to a sequential-style programming model where events are processed one at a time, and have the ability to access the current memory image of the whole simulation model, namely the collection of the states of any involved object. This can strongly simplify the development of specific models, e.g., by avoiding the need for passing state information across concurrent objects in the form of events. In this article we investigate on both programmability and performance aspects related to developing/supporting a multi-agent exploration model on top of the ROOT-Sim PDES platform, which supports ECS

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape