Team Cooperation Using Dual Dynamics

Bredenfeld, Ansgar; Kobialka, Hans-Ulrich

doi:10.1007/3-540-44568-4_7

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…A distributed approach that is not based on explicit communication is described in Ferraresso et al (2000), in which two robots are able to exchange the ball by using direct communication of their roles and a fieldvector based collision avoidance that takes into account in a proper way the role of the robots. Other behavior based reactive MRS are presented in Brendenfeld and Kobialka (2000) and Carpin et al (2000). Our approach exhibits the following two features: on one hand, it exploits explicit communication among the robots for exchanging information about the status of the environment in order to achieve a distributed agreement on the actions to be performed; on the other hand, it is robust to possible communication failures, since in these cases the system degrades its performance, but it still keeps on the execution of the task.…”

Section: Related Workmentioning

confidence: 99%

Untitled

et al. 2003

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Coordination in multi-robot systems is a very active research field in Artificial Intelligence and Robotics, since through coordination one can achieve a more effective execution of the robots' tasks. In this paper we present an approach to distributed coordination of a multi-robot system that is based on dynamic role assignment. The approach relies on the broadcast communication of utility functions that define the capability for every robot to perform a task and on the execution of a coordination protocol for dynamic role assignment. The presented method is robust to communication failures and suitable for application in dynamic environments. In addition to experimental results showing the effectiveness of our approach, the method has been successfully implemented within the team of heterogeneous robots Azzurra Robot Team in a very dynamic hostile environment provided by the RoboCup robotic soccer competitions.

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Section: Related Workmentioning

confidence: 99%

Untitled

et al. 2003

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“…The approach in [16] represents an example of reactive, weakly coordinated system, in which two robots are able to exchange the ball by using direct communication of their roles and a field-vector based collision avoidance that takes into account in a proper way the role of the robots. Other behavior based reactive MRS are presented in [7,10] On the other hand, successful deliberative centralized approaches in the Middle-Size league are used when it is possible to reliably reconstruct global information about the environment. For example the CS Freiburg team [19] makes use of very precise measurements given by laser scanners to reconstruct a global representation of the field and a social deliberative weakly centralized approach is obtained by dynamic selection of the robot that acts as the leader.…”

Section: Soccermentioning

confidence: 99%

Reactivity and Deliberation: A Survey on Multi-Robot Systems

Iocchi

Nardi

Salerno

2001

Balancing Reactivity and Social Deliberation in Multi-Agent Systems

103

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Abstract. Multi Robot Systems are, nowadays, an important research area within Robotics and Artificial Intelligence. Although Multi Robot Systems can be regarded as a particular case of Multi Agent Systems, it seems appropriate to study the Multi Robot Systems from a specific viewpoint, because of the issues which arise from the embodiment of agents operating in real environments. In this paper, we present an analysis of Multi Robot Systems by looking at their cooperative aspects. In particular, we propose a taxonomy of Multi Robot Systems and a characterization of reactive and social deliberative behaviors of the Multi Robot System as a whole. Finally, we address some Multi Robot Systems, which we consider representative of the various nodes in our taxonomy.

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“…To achieve that, we have implemented g b in Eq. 2 as a bistable ground form (see [BK01] Sec. 4 for details) providing some hysteresis effect.…”

Section: Bbss As Dynamical Systemsmentioning

confidence: 99%

“…DD has recently been extended with team variables [BK01], which are exported by a behavior system, so that other behavior systems (robots) can read their current values. For every team variable only the exporting behavior systems is allowed to change its value, for all others it's value is read-only.…”

Section: Bbss As Dynamical Systemsmentioning

confidence: 99%

Advances in Plan-Based Control of Robotic Agents

Beetz¹,

Hertzberg²,

Ghallab³

et al. 2002

Lecture Notes in Computer Science

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PrefaceIn recent years, autonomous robots, including Xavier, Martha [1], Rhino [2,3], Minerva, and Remote Agent, have shown impressive performance in long-term demonstrations. In NASA's Deep Space program, for example, an autonomous spacecraft controller, called the Remote Agent [5], has autonomously performed a scientific experiment in space. At Carnegie Mellon University, Xavier [6], another autonomous mobile robot, navigated through an office environment for more than a year, allowing people to issue navigation commands and monitor their execution via the Internet. In 1998, Minerva [7] acted for 13 days as a museum tourguide in the Smithsonian Museum, and led several thousand people through an exhibition.These autonomous robots have in common that they rely on plan-based control in order to achieve better problem-solving competence. In the plan-based approach, robots generate control actions by maintaining and executing a plan that is effective and has a high expected utility with respect to the robots' current goals and beliefs. Plans are robot control programs that a robot can not only execute but also reason about and manipulate [4]. Thus, a plan-based controller is able to manage and adapt the robot's intended course of action -the plan -while executing it and can thereby better achieve complex and changing tasks. The plans used for autonomous robot control are often reactive plans, that is, they specify how the robots are to respond in terms of low-level control actions to continually arriving sensory data in order to accomplish their objectives. The use of plans enables these robots to flexibly interleave complex and interacting tasks, exploit opportunities, quickly plan their courses of action, and, if necessary, revise their intended activities.The emergence of complete plan-based robot control systems, on the one hand, and the lack of integrated computational models of plan-based control, on the other hand, motivated us to organize a Dagstuhl seminar "Plan-Based Control of Robotic Agents" 1 at Schloss Dagstuhl (21-26 October 2001). The purpose of this seminar was to bring together researchers from different fields in order to promote information exchange and interaction between research groups working on various aspects of plan-based autonomous robot control.The objective was to identify computational principles that enable autonomous robots to accomplish complex, diverse, and dynamically changing tasks in challenging environments. These principles include plan-based high-level control, probabilistic reasoning, plan transformation, and context and resourceadaptive reasoning. The development of comprehensive and integrated computational models of plan-based control requires us to consider different aspects of plan-based control -plan representation, reasoning, execution, and learningtogether and not in isolation. Such integrated approaches enable us to exploit synergies between the different aspects and thereby come up with simpler and more powerful computational models.1 For more information on the Se...

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Team Cooperation Using Dual Dynamics

Cited by 14 publications

References 10 publications

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Reactivity and Deliberation: A Survey on Multi-Robot Systems

Advances in Plan-Based Control of Robotic Agents

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