Abstract—This paper presents a method that integrates MATLAB into a distributed behavioral robotics architecture. The architecture is written in Java and uses the Jini platform for distributed object registration, lookup and remote method invocation. The method described here can be used to integrate MATLAB into any Java-based behavioral architecture. The form of the integration allows a running MATLAB workspace to be accessed as a distributed object within the larger Java/Jini-based architecture. This is beneficial because MATLAB scripts and functions may be called in interpreted form and can make full use of MATLAB tool boxes and have access to the MATLAB workspace environment. This is not possible when MATLAB scripts are compiled into stand-alone C++, Java or p-code. The use of the architecture is demonstrated on an iRobot ATRV-JR robot and remote computer workstation. Experiments have been conducted to quantify GPS and odometry errors in outdoor environments using automated methods supported by the distributed architecture. Keywords-mobile Distributed architecture; autonomous robot control; control architecture; MATLAB; Jini; GPS-based navigation; I.

Abstract — Design, implementation and experimental validation of a MATLAB based autonomous robot control framework is presented, supported by, and integrated into a distributed field robot architecture known as distributed-SFX. The MATLAB based framework is composed of multi sensor fuzzy logic robot controllers that utilize laser, GPS and odometer data, fusing such sensor data and filtering out noise, to improve collision free navigation. Extensive outdoor environment experiments with single and multiple mobile robots are performed to demonstrate waypoint and goal point navigation, and raster scan search patterns in unknown environments with static and dynamic obstacles. Results and videos are provided to justify the proposed approach. I.

Abstract — Self-organizing systems could serve as a solution for many technical problems where properties like robustness, scalability, and adaptability are required. However, despite all these advantages and due to the decentralized control there is no straight-forward way to design such a system. In this paper we describe a novel design approach using genetic algorithms and artificial neural networks to automatize the part of the design process that requires most of the time. A simulated robot soccer game was implemented to test and evaluate the proposed method. A new approach in evolving competitive behavior is also introduced using Swiss System instead of the full tournament to cut down the number of necessary simulations.

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics

Abstract- AI in computer games has been highlighted in recent, but manual designing costs a great deal. An evolutionary algorithm has developed strategies to play games without using features that would commonly require the knowledge of developers. Since the real-time reactive selection of behaviors for NPCs is required for better playing, a reactive behavior system consisting neural networks is presented. Using only the raw information on games, the evolutionary algorithm optimizes the reactive behavior system based on a co-evolutionary method. For demonstration of the proposed method, we have developed a real-time simulation game called ‘Build & Build’. As the results, we have obtained emergent and interesting behaviors that are adaptive to the environment, and confirmed the applicability of evolutionary approach to designing NPCs ’ behaviors without relying on human expertise. 1

(DARPA) or the Department of Defense. DARPA does not guarantee the accuracy or reliability of the information in this paper. Autonomous vehicles are complex systems with many interacting hardware and software components operating in an uncertain and dynamic environment. Organizational principles and procedures are described which help assure reliable and intelligent actions on the part of the vehicle. This includes both high-level system models, as well as process level monitoring and testing to verify and validate the system components on the fly. We propose a high-level model based on a probabilistic characterization of the inputs and outputs (or other observable elements) of the modules, and for individual components, we propose to exploit Instrumented Logical Sensors. These methodologies are to be demonstrated in the context of the autonomous vehicle. 1.0 Introduction and Overview The 2007 DARPA Urban Challenge (DUC) is a competition that requires driverless cars to navigate through an urban environment. The 2007 DUC is a continuation of the 2005

This article presents fundamental aspects of a multilayer, hybrid, distributed field robot architecture (DFRA), implemented in Java using Jini to manage distributed objects, services, and modules between robots and other system components [39]. It is designed for heterogeneous teams of unmanned robots operating in uncertain/hostile environments. Emphasis is given to the control theoretic lower-level MATLAB-based environment (supported and integrated into the Java-based framework using the JMatLink Java class library [30]), experimentally validated by implementing simple prototype support modules for outdoor mobile robot navigation. Derivation, implementation, and testing of such a distributed architecture bring together the diverse fields of distributed artificial intelligence, human robot interaction, and multiagent systems, combined with control theoretic approaches. This is where the main contribution and novelty of this article lies: although the DFRA is implemented in Java/Jini, the MATLAB environment allows for mathematical control theoretic research and experimentation and for rapid prototyping of behavioral and control modules and services. Wrapping the MATLAB workspace environment with JMatLink, in conjunction with the Jini distributed object platform, allows modules and services implemented as native interpreted MATLAB code to be accessed

Summary. The activities of social insects are often based on a self-organising process, that is, “a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system”(see [4], p. 8). In a self-organising system such as an ant colony, there is neither a leader that drives the activities of the group, nor are the individual ants informed about a global recipe or blueprint to be executed. On the contrary, each single ant acts autonomously following simple rules and locally interacting with the other ants. As a consequence of the numerous interactions among individuals, a coherent behaviour can be observed at the colony level. A similar organisational structure is definitely beneficial for a swarm of autonomous robots. In fact, a coherent group behaviour can be obtained providing each robot with simple individual rules. Moreover, the features that characterise a self-organising system—such as decentralisation, flexibility and robustness—are highly desirable also for a swarm of autonomous robots. The main problem that has to be faced in the design of a self-organising robotic system is the definition of the individual rules that lead to the desired collective behaviour. The solution we propose to this design problem relies on artificial evolution as the main tool for the synthesis of self-organising behaviours. In this chapter, we provide an overview of successful applications of evolutionary techniques to the evolution of self-organising behaviours for a group of simulated autonomous robots. The obtained results show that the methodology is viable, and that it produces behaviours that are efficient, scalable and robust enough to be tested in reality on a physical robotic platform. 1

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