A Survey and Analysis of Multi-Robot Coordination

International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper

Evaluating Human-robot Implicit Communication Through Human-human Implicit Communication

Human-Robot Interaction (HRI) research is examining ways to make human-robot (HR) communication more natural. Incorporating natural communication techniques is expected to make HR communication seamless and more natural for humans. Humans naturally incorporate implicit levels of communication, and including implicit communication in HR communication should provide tremendous benefit. The aim for this work was to evaluate a model for humanrobot implicit communication. Specifically, the primary goal for this research was to determine whether humans can assign meanings to implicit cues received from autonomous robots as they do for identical implicit cues received from humans. An experiment was designed to allow participants to assign meanings to identical, implicit cues (pursuing, retreating, investigating, hiding, patrolling) received from humans and robots. Participants were tasked to view random video clips of both entity types, label the implicit cue, and assign a level of confidence in their chosen answer. Physiological data was tracked during the experiment using an electroencephalogram and eye-tracker. Participants answered workload and stress measure questionnaires following each scenario. Results revealed that participants were significantly more accurate with human cues (84%) than with robot cues (82%), however participants were highly accurate, above 80%, for both entity types. Despite the high accuracy for both types, participants remained significantly more confident in answers for humans (6.1) than for robots (5.9) on a confidence scale of 1 - 7. Subjective measures showed no significant differences for stress or mental workload across entities. Physiological measures were not significant for the engagement index across v entity, but robots resulted in significantly higher levels of cognitive workload for participants via the index of cognitive activity. The results of this study revealed that participants are more confident interpreting human implicit cues than identical cues received from a robot. However, the accuracy of interpreting both entities remained high. Participants showed no significant difference in interpreting different cues across entity as well. Therefore, much of the ability of interpreting an implicit cue resides in the actual cue rather than the entity. Proper training should boost confidence as humans begin to work alongside autonomous robots as teammates, and it is possible to train humans to recognize cues based on the movement, regardless of the entity demonstrating the movement

Cooperative behaviors in multi-robot systems through implicit communication

We illustrate the Cooperation through Implicit Communication behavior-based approach used for developing the PaSo-Team (The University of Padua Simulated Soccer Robot Team), a multi-robot software system for soccer robot competitions promoted by the RoboCup Simulation League. The configuration of the environment, namely the robots' relative positions depending on both the global task and the game dynamics, provides a source of implicit information about the robots' intention to be involved in collective actions, making them able to cooperate implicitly. The soccer team performance can be tuned by triggering the arbitration module of any single robot to generate, as many as possible, suitable situations which hint to the team the action of scoring the goal. Some macroscopic parameters can be usefully introduced to evaluate the evolution of the whole multi-robot software system

Cooperative behaviors in multi-robot systems through implicit communication

5reservedmixedPAGELLO E; D'ANGELO A; MONTESELLO F; GARELLI F; FERRARI CPagello, E; D'Angelo, Antonio; Montesello, F; Garelli, F; Ferrari, C

SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIOR

Swarm intelligence in multi-robot systems has become an important area of research within collective robotics. Researchers have gained inspiration from biological systems and proposed a variety of industrial, commercial, and military robotics applications. In order to bridge the gap between theory and application, a strong focus is required on robotic implementation of swarm intelligence. To date, theoretical research and computer simulations in the field have dominated, with few successful demonstrations of swarm-intelligent robotic systems. In this thesis, a study of intelligent foraging behavior via indirect communication between simple individual agents is presented. Models of foraging are reviewed and analyzed with respect to the system dynamics and dependence on important parameters. Computer simulations are also conducted to gain an understanding of foraging behavior in systems with large populations. Finally, a novel robotic implementation is presented. The experiment successfully demonstrates cooperative group foraging behavior without direct communication. Trail-laying and trail-following are employed to produce the required stigmergic cooperation. Real robots are shown to achieve increased task efficiency, as a group, resulting from indirect interactions. Experimental results also confirm that trail-based group foraging systems can adapt to dynamic environments

Strategy model for multi-robot coordination in robotic soccer

Journal home page: http://www.ttp.net/1660-9336.html[EN] Soccer robots have been frequently used to validate models of multi-agent systems, involving collaboration among the agents. For this purpose, many researchers in robotics have been developing robotic soccer teams which compete in events such as RoboCup. This paper presents a strategy model for multi-robot coordination in robotic soccer teams involving ball position, team member position and opponent position for the selection of a team tactic and the player roles. This assignation is dynamical and achieved by a virtual coach. This strategy model was validated in a RoboCup Small Size League environment using Webots robot simulatorThis project is partially funded by the Ministry of Higher Education Malaysia under the grant number 600-RMI/ERGS 5/3 (23/2011)Guarnizo, JG.; Mellado Arteche, M.; Low, CY.; Aziz, N. (2013). Strategy model for multi-robot coordination in robotic soccer. Applied Mechanics and Materials. 393:592-597. doi:10.4028/www.scientific.net/AMM.393.592S592597393E. Pagello, A. D'Angelo, F. Montesello, F. Garelli, C. Ferrari, Cooperative Behaviors in Multi-Robot Systems through Implicit Communication, Robotics and Autonomous Systems, 29(1), (1999) 65–77.M. Isik, F. Stulp, G. Mayer, H. Utz, Coordination without Negotiation in Teams of Heterogeneous Robots, RoboCup 2006: Robot Soccer World Cup X, eds., G. Lakemeyer, E. Sklar, D. Sorrenti, and T. Takahashi, LNAI, Springer-Berlin, 4434 (2007).F. Stulp, M. Isik, M. Beetz, Implicit Coordination in Robotic Teams using Learned Prediction Models, ICRA, IEEE ICRA, New York, 2006, p.1330–1335.O. Obst, J. Boedecker, Flexible Coordination of Multiagent Team Behavior using HTN Planning, RoboCup 2005: Robot Soccer World Cup IX, eds., I. Noda, A. Jacoff, A. Bredenfeld, and Y. Takahashi, Springer, Berlin, 2006, p.521– 528.C.Y. Low, N. Aziz, M. Aldemir, R. Dumitrescu, H. Anacker, M. Mellado, Strategy Planning for Collaborative Humanoid Soccer Robots based on Principle Solution, Production Engineering, 7(1) (2013) 23-34.A. K. Mackworth, On seeing robots, Computer Vision: Systems, Theory, and Applications, A. Basu and X. Li, Eds. Singapore: World Scientific, 1993, p.1–13.J.G. Guarnizo, J. F. Blanes, M. Mellado, J. Simo, M Muñoz, A Survey of Team Strategies in Robot Soccer. Focused in Standard Platform League. XIII Workshop on phisicall agents, Santiago de Compostela, (2012).H. Birkhofer, Analyse und Synthese der Funktionen Technischer Produkte,. Dissertation, Technische Universität Braunschweig, (1980).G. Langlotz, Ein Beitrag zur Funktionsstrukturentwicklung Innovativer Produkte,. Dissertation, Institut fuer Rechneranwendung in Planung und Konstruktion, Universitaet Karlsruhe, Shaker-Verlag, Band 2/2000, Aachen, (2000)