Architecture for Multi-robot Systems with Emergent Behavior

This thesis focuses on the development of a system in which a team of heterogeneous mobile robots can cooperate to perform a wide range of tasks. In order that a group of heterogeneous robots can cooperate among them, one of the most important parts to develop is the creation of an architecture which gives support for the cooperation. This architecture is developed by means of embedding agents and interfacing agent code with native low-level code. It also addresses the implementation of resource sharing among the whole group of robots, that is, the robots can borrow capabilities from each-other.

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012

In the field of robotics, typical, single-robot systems encounter limits when executing complex tasks. Todays systems often lack flexibility and inter-operability, especially when interaction between participants is necessary. Nevertheless, well developed systems for robotics and for the cognitive and distributive domain are available. What is missing is the common link between these two domains. This work deals with the foundations and methods of a middle layer that joins a multi-agent system with a multi-robot system in a generic way. A prototype system consisting of a multiagent system, a multi-robot system and a middle layer will be presented and evaluated. Its purpose is to combine highlevel cognitive models and information distribution with robotfocused abilities, such as navigation and reactive behavior based artificial intelligence. This enables the assignment of various scenarios to a team of mobile robots.

ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005., 2005

Large number of autonomous robot solutions exists for various missions and domains. These robots are sufficient for the missions they are built for. At the same time each of them has limited functional and physical capabilities. Multi robot systems can be used to remove these limits. However it is true only in case when the system ensures effective interaction among the robots i.e. enables their social behaviour. Usually it is hard to implement such capabilities directly into robots due to functional and physical limitations and heterogeneity of the team. One of possible solutions is to implement management systems outside the robots. It should decompose tasks, allocate subtasks to specific robots and monitor the execution of the assigned tasks. In order to avoid inherent drawback of fully centralized systems a significant level of autonomy has to be preserved. Intelligent agents fulfil these requirements. Therefore we propose a general multi-agent system's architecture for mult...

2021

In most autonomous robot approaches, the individual robot’s goals and cooperation behavior are fixed during the design. Moreover, the robot’s design may limit its ability to perform other than initially planned tasks. This leaves little room for novel dynamic cooperation where new (joint) actions could be formed or goals adjusted after deployment. In this paper, we address how situational context augmented with peer modeling can foster cooperation opportunity identification and cooperation planning. As a practical contribution, we introduce our new software architecture that enables developing, training, testing, and deploying dynamic cooperation solutions for diverse autonomous robots. The presented architecture operates in three different worlds: in the Real World with real robots, in the 3D Virtual World by emulating the real environments and robots, and in an abstract 2D Block World that fosters developing and studying large-scale cooperation scenarios. Feedback loops among thes...

2002

In this paper the development and the implementation of one new robotic architecture for the coordination and the planning of a robot colonies in dangerous and unknown environments are outlined. The name of this new architecture is Metaphor of Italian Politics (MIP Architecture). The structure of this architecture is dynamics. It takes inspiration from the political organizations of the democratic governments, where the leader isn't only one robot but it is constituted by a government of robots. In the MIP architecture the robots team is coordinated by a Prime Minister, a Minister of the Defence and a Minister of Communication while a second group of robots, the Robot Citizens, are the executors of each mission. The model of the agents is hybrid (reactive and deliberative), so every robot can assume every political position inside this dynamic structure. An election procedure for the government regeneration has been developed in order to avoid the collapse of a mission and improve the robot colony performances. To validate the effectiveness of the our approach we have developed a framework based on the Mission Lab software developed at the Mobile Robot Lab of the Georgia Institute of Technology.

This paper aims to present the Multi-Agent System to Control and Coordinate teAmworking Robots (MAS2CAR), a new architecture to control a group of coordinated autonomous robots in unstructured environments. MAS2CAR covers two main layers: (i) the Control Layer and we focus on (ii) the Coordination Layer. The control module is responsible for a part of the decision making process while taking into account robot's structural constraints. Despite this autonomy possibility, the Coordination Layer manages the robots in order to bring cooperative behavior and to allow teamwork. In this paper we present a scenario validating our approach based upon the multi-agent systems (MAS). Thanks to its reliability we have chosen the M OISE Inst organizational model and we will present how it can be used for this use-case. Moreover, regarding to the implementation part, we have retained U TOPIA, a framework which automatically build a MAS thanks to a M OISE Inst specification.

This paper presents a generic architecture for the operation of a team of autonomous robots to achieve complex missions. Its interest stems from its ability to provide a framework for cooperative decisional processes at different levels : high level plan synthesis, task allocation and task achievement. It is based on a combination of local individual planning and coordinated decision for incremental plan adaptation to the multi-robot context. Indeed, we claim that it is often possible (and useful) to treat these three issues separately. As we will see, this levels deal with problems of different nature, leading to specific representations, algorithms and protocols.

2001

This paper presents a model for the control of autonomous robots that allows cooperation among them. The control structure is based on a general purpose multi-agent architecture using a hybrid approach made up by two levels. One level is composed of reactive skills capable of achieving simple actions by their own. The other one uses an agenda used as an opportunistic planning mechanism to compound, activate and coordinate the basic skills. This agenda handles actions both from the internal goals of the robot or from other robots. This two level approach allows the integration of real-time response of reactive systems needed for robot low-level behavior, with a classical high level planning component that permits a goal oriented behavior. The paper describes the architecture itself, and its use in three different domains, including real robots, as well as the issues arising from its adaptation to the RoboCup simulator domain.

Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering, 2015

Multi-Robot System (MRS) is considered as a particular form of Multi Agent System (MAS) by specifically addressing planning and social abilities. The design of autonomous robots includes the design of team behaviors constituted by several intelligent agents each one has to interact with the other autonomous robots. The problem faced is how to ensure a distributed planning through the cooperation of the distributed robotic agents. To do so, we propose to use the conept of five capabilities model which is based on Environment, Self, Planner, Competence and Communication. We illustrate our line of thought with a Benchmark Production System used as a running example to explain our contribution.

Intelligent Autonomous Systems, 2004

Teams of robots are increasingly deployed in real world applications. One of the key challenges in building such teams is to automate the control of teamwork, such that the designer can concentrate her efforts on the taskwork to be done. This paper presents steps towards a novel framework addressing this challenge in teams of behavior-based agents. The framework provides a rich representation that facilitates management of teamwork knowledge, and separates behaviors that govern a robot's interaction with its task from behaviors that govern a robot's interaction with its teammates. In addition, the paper presents a set of algorithms that control the execution and communication of behaviors, to automate synchronization and allocation of behaviors to sub-teams. We describe our implementation of the framework in the BITE architecture, a distributed behavior-based architecture providing automated coordination and collaboration services in a team of Sony AIBO robots.

2015

In this paper we propose an algorithm for coordinating a group of mobile robots that go through predefined paths in a dynamic industrial workplace. The coordination is characterized by a decoupled approach. Then, a behavior-based architecture be used as the underlying control representation providers a useful encoding that lends robustness to control. Some robot behaviors be designed to support for accomplishing industrial task. Four Lego Mindstorms robots used to implement the proposed algorithm. This research tackles the coordination movement issue in material handling in order to minimize the delivery time. Several experiment have been done to know performances of the system. The promising results have been proved that the proposed control architecture has better capability to accomplish useful task in real industrial-like environment.

2000

Kluwer Academic Publishers (ISBN 1-4020-3388-5) This proceedings volume documents recent cutting-edge developments in multi-robot Systems research. This volume is the result of the Third International workshop on Multi-Robot Systems that was held in March 2005 at the Naval Research Laboratory in Washington, D.C. This workshop brought together top researchers working in areas relevant to designing teams of autonomous vehicles, including robots and unmanned ground, air, surface, and undersea vehicles. The workshop focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy,

This paper presents an approach for multi-robot coordination based both on coordinated navigation and task allocation method. An ad hoc agent based architecture is defined in order to implement the robot control system in both simulation and real applications. The coordination of the multi-robot system is based on agent interaction and negotiation, and a communication infrastructure based on open web standards is provided. The system employs the RFID technology for building a context aware information system which is the base of the coordination strategies.

Advanced Robotics, 2003

We investigate the problem of how to make a multi-robot system performing a cooperative task by inducing a set of emergent actions. We model the environment dynamics by considering some parameters that express the ability of each robot to perform its task. Thus, the members of a group of robots become aware of their ability to realize some tasks by simply computing some quality function Q of the configuration pattern of the environment. A role assignment schema allows roles to be swapped among the robots of the group in order to select the best behaviors able to perform the task cooperatively. We illustrate this approach by showing how two soccer robots were able to exchange a ball, during a real game, by combining the use of efficient collision avoidance algorithms with role swapping triggered by the value of the above quality function Q.