There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting collective behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of collective robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations. 1

Abstract | In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We showthat the autonomous development of a set of behaviors for locating a battery charger and periodically returning to it can be achieved by lifting constraints in the design of the robot/environment interactions that were employed in a preliminary experiment. The emergent homing behavior is based on the autonomous development ofaninternal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy.

This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising directions. 1 Introduction This paper is concerned with the distant goal of automated synthesis of robot controllers. Specifically, we focus on the problems of evolving controllers for physically embodied and embedded systems that deal with all of the noise and uncertainly present in the world. We will also address some systems that evolve both the morphology and the controller of a robot. Within the scope of this paper we define morphology as the physical, embodied characteristics of the robot, such as its mechanics and sensor organization. Given that definition, the only examples of evolving both morphology and control exist in simulation. Evolutionary methods for automated hardware design are an ...

In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution.

this paper we present a set of experiments in which a group of simulated robots were evolved for the ability to aggregate and to move together toward a light target

. In this paper we explain how we applied genetic programming to behavior-based team coordination in the RoboCup Soccer Server domain. Genetic programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast to other learning methods, genetic programming's automatic programming makes it a natural approach for developing algorithmic robot behaviors. The RoboCup Soccer Server was a very challenging domain for genetic programming, but we were pleased with the results. At the end, genetic programming had produced teams of soccer softbots which had learned to cooperate to play a good game of simulator soccer. 1 Introduction The RoboCup competition pits robots against each other in a simulated soccer tournament [Kitano et al, 1995]. The aim of the RoboCup competition is to foster an interdisciplinary approach to robotics and agent-based Artificial Intelligence by presenting a domain that requires large-scale cooperation and c...

Cooperative multi-agent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to multi-agent systems problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources. 1

To my mother and father, and to my wife. iii Acknowledgements I would like to take this opportunity to express my gratitude to the people who have helped and supported me during my Ph.D. program. First and foremost, I am particularly grateful to my adviser, Professor Demetri Terzopoulos. It was his guidance, encouragement and collaboration that lead me along the bumpy road of Ph.D. study to this final accomplishment. I am so fortu-nate to have had the experience of research and study with him for the past five years, which has changed me and will be influencing me for the rest of my life. Next, I would like to thank Professors Ken Perlin, Davi Geiger, Yann LeCun, Denis Zorin and Chris Bregler for serving on my proposal and dissertation com-mittees. Special thanks go to Ken for his insightful opinions and suggestions on my research work. I owe a lot to my colleagues and lab mates, among them Mauricio Plaza who worked on the reconstructed Penn Station model with me, Alex Vasilescu, Sung-Hee Lee and Evgueni Parilov who shared their ideas, opinions, discussion and jokes with me, and everybody at the Media Research Lab for the discussions, laughter, food and drink. The research reported herein was supported in part by grants from the Defense iv

Genetic Programming is applied to the task of evolving general iterative sorting algorithms. A connection between size and generality was discovered. Adding inverse size to the fitness measure along with correctness not only decreases the size of the resulting evolved algorithms, but also dramatically increases their generality and thus the effectiveness of the evolution process. In addition, a variety of differing problem formulations are investigated and the relative probability of success for each is reported. An example of an evolved sort from each problem formulation is presented, and an initial attempt is made to understand the variations in difficulty resulting from these differing problem formulations. 1 Introduction In order to further the application of Genetic Programming to evolution of complex algorithms, the work reported here explores the impact of differing problem formulations and fitness measures on the likelihood of evolving a general sorting algorithm on a given G...

this paper is composed of two main parts. A global navigation system based on L-systems, and a local navigation system coupled with a walk motor. These sub-systems are presented with examples and result in Sections 3 and 4. The last section will present the future integration of the two sub-systems and the evolution of our vision-based navigation system