There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative 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 cooperative 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.

Cooperative mobile robotics: antecedents and directions

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, the authors 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. The authors describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.

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We present a distributed algorithm for converging autonomous mobile robots with limited visibility toward a single point. Each robot is an omnidirectional mobile processor that repeatedly: 1) observes the relative positions of those robots that are visible; 2) computes its new position based on the observation using the given algorithm; 3) moves to that position. The robots' visibility is limited so that two robots can see each other if and only if they are within distance V of each other and there are no other robots between them. Our algorithm is memoryless in the sense that the next position of a robot is determined entirely from the positions of the robots that it can see at that moment. The correctness of the algorithm is proved formally under an abstract model of the robot system in which: 1) each robot is represented by a point that does not obstruct the view of other robots; 2) the robots' motion is instantaneous; 3) there are no sensor and control error; 4) the issue of collision is ignored. The results of computer simulation under a more realistic model give convincing indication that the algorithm, if implemented on physical robots, will be robust against sensor and control error.

Distributed memoryless point convergence algorithm for mobile robots with limited visibility

The objective of this paper is to describe the development of a specific theory of interactions and learning among multiple robots performing certain tasks. One of the primary objectives of the research was to study the feasibility of a robot colony in achieving global objectives, when each individual robot is provided only with local goals and local information. In order to achieve this objective the paper introduces a novel cognitive architecture for the individual behavior of robots in a colony. Experimental investigation of the properties of the colony demonstrates its ability to achieve global goals, such as the gathering of objects, and to improve its performance as a result of learning, without explicit instructions for cooperation. Since this architecture is based on representation of the “likes” and “dislikes” of the robots, it is called the Tropism System Cognitive Architecture. This paper addresses learning in the framework of the cognitive architecture, specifically, phylogenetic and ontogenetic learning by the robots. The results show that learning is indeed possible with the Tropism Architecture, that the ability of a simulated robot colony to perform a gathering task improves with practice and that it can further improve with evolution over successive generations. Experimental results also show that the variability of the results decreases over successive generations.

Phylogenetic and Ontogenetic Learning in a Colony of Interacting Robots

Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years. The main concern is to find the effective coordination among autonomous agents to perform the task in order to achieve a high quality of overall performance. Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS). The problems, issues, and directions of MARS research have been investigated in the literature reviews. Three main elements of MARS which are the type of agents, control architectures, and communications were discussed thoroughly in the beginning of this paper. A series of problems together with the issues were analyzed and reviewed, which included centralized and decentralized control, consensus, containment, formation, task allocation, intelligences, optimization and communications of multi-agent robots. Since the research in the field of multi-agent robot research is expanding, some issues and future challenges in MARS are recalled, discussed and clarified with future directions. Finally, the paper is concluded with some recommendations with respect to multi-agent systems.

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A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions

Deals with a computer simulation study of the cooperative behavior of multirobots for investigating the characteristics of swarm intelligence generated by multirobot system. Individual robots in the multirobot system are equipped with low-level intelligence to detect obstacles, steer, proceed, and undertake a hello-call communication. This paper first clarifies the characteristics of multirobot behavior under free traveling without hello-call communication and then the characteristics of hello-call communication with respect to message transmission power over whole member robots. Introducing a marker robot to the multi-robot system, the authors find that the multirobot system can generate a communication chain to be used for searching the route to their goal. They demonstrate the swarm intelligence of the multirobots system in the case of route-searching and show the effects of robot population on the swarm intelligence.

Cooperative route-searching behavior of multi-robot system using hello-call communication

This paper deals with the effects of robot population density on the emergence of circle formation in multiple robots system. Individual robot has a specific range of signal communication to detect other robots. According to the information about the location of one or two robots detected by the signal communication within the range, the robot takes an action such as proceeding forward or turning left/right at each time step. Implementing this action selection algorithm to each of the robots, we carry out the computer simulation of global behaviors in the multiple robots system, and we find that there exists a certain condition under which the circle formation emerges in the multiple robots system. To investigate the effects of the robot population involved and the size of the simulation area on the characteristics of the circle formation, the computer simulation was made and it clarified that the emergence of the circle formation in the multiple robots system is strongly influenced by the robot population density rather than to the robot population and the simulation area size.

S. Ichikawa

Papers

Cooperative route-searching behavior of multi-robot system using hello-call communication

Effects of population density on the emergence of circle formation in multiple robots system with a local vision