Optimization of rules selection for robot soccer strategies

Mobile embedded systems belong among the typical applications of distributed systems control in realtime. An example of a mobile control system is a robotic system. The proposal and realization of such a distributed control system represents a demanding and complex task for real-time control. In the process of robot soccer game applications, extensive data is accumulated. The reduction of such data is a possible win in a game strategy. The main topic of this article is a description of an efficient method for rule selection from a strategy. The proposed algorithm is based on the geometric representation of rules. A described problem and a proposed solution can be applied to other areas dealing with effective searching of rules in structures that also represent coordinates of the real world. Because this construed strategy describes a real space and the stores physical coordinates of real objects, our method can be used in strategic planning in the real world where we know the geographical positions of objects.Web of Science11art. no. 1

A Mobile Robot Project

We are building a mobile robot which will roam around the AI lab observing and later perhaps doing. Our approach to building the robot and its controlling software differs from that used in many other projects in a number of ways. (1) We model the world as three dimensional rather than two. (2) We build no special environment for our robot and insist that it must operate in the same real world that we inhabit. (3) In order to adequately deal with uncertainty of perception and control we build relational maps rather than maps embedded in a coordinate system, and we maintain explicit models of all uncertainties. (4) We explicitly monitor the computational performance of the components of the control system, in order to refine the design of a real time control system for mobile robots based on a special purpose distributed computation engine. (5) We use vision as our primary sense and relegate acoustic sensors to local obstacle detection. (6) We use a new architecture for an intelligent system designed to provide integration of many early vision processes, and robust real-time performance even in cases of sensory overload, failure of certain early vision processes to deliver much information in particular situations, and computation module failure.MIT Artificial Intelligence Laborator

Human-computer cooperation platform for developing real-time robotic applications

[EN] This paper presents a human-computer cooperation platform, which permits the coordination between the user and the tool to improve the development of real-time control applications (e.g., mobile robots). These applications have functional (robot objectives) and temporal requirements to accomplish (deadlines guarantee of tasks). The simulation tool has been designed in order to permit the testing and validation of these two requirements. To this end, the tool is composed of two independent simulators interconnected through a shared memory: the robot simulator (functional level) and the real-time task scheduler simulator (task execution level). Robotic applications can be defined with the robot simulator while the real-time scheduler simulator permits to analyze the schedulability of the robotic tasks. The real-time task simulator incorporates a flexible task model where the task temporal parameters (e.g., computation time) adapt to the requirements of the application (e.g., number of objects in scenes); thus, the use of the CPU is not overestimated. A key issue of the framework is the human-computer interface, which allows the monitoring of different parameters of the application: robot objectives, task schedule, robot speed, computation time, CPU utilization, deadline misses. The usefulness of the simulation tool is shown through different robotic navigation experiments. Finally, the simulation tool has been used to evaluate the proposed flexible model of tasks compared to a traditional fixed temporal parameters task model. Results show that the robot fulfills the objectives earlier, about 32% on average, and consumes on average about 15% less CPU to accomplish the objectives.Domínguez Montagud, CP.; Martínez-Rubio, J.; Busquets Mataix, JV.; Hassan Mohamed, H. (2019). Human-computer cooperation platform for developing real-time robotic applications. The Journal of Supercomputing. 75(4):1849-1868. https://doi.org/10.1007/s11227-018-2343-4S18491868754Dominguez C, Hassan H, Crespo A (2007) Real-time embedded architecture for pervasive robots. In: The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007), pp 531–536Audsley NC, Burns A, Davis RI, Tindell KW, Wellings AJ (1995) Fixed priority pre-emptive scheduling: an historical perspective. Real Time Syst 8(2–3):173–198Stankovic JA, Lee I, Mok A, Rajkumar R (2005) Opportunities and obligations for physical computing systems. Computer 38(11):23–31Zhen Z, Qixin C, Lo C, Lei Z (2009) A CORBA-based simulation and control framework for mobile robots. Robotica 27(3):459Ferretti G, Magnani G, Porrati P, Rizzi G, Rocco P, Rusconi A (2008) Real-time simulation of a space robotic arm. In: IROSQadi A, Goddard S, Huang J, Farritor S (2005) A performance and schedulability analysis of an autonomous mobile robot. In: 17th Euromicro Conference on Real-Time Systems (ECRTS’05), pp 239–248Goud GR, Sharma N, Ramamritham K, Malewar S (2006) Efficient real-time support for automotive applications: a case study. In: 12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’06), pp 335–341Pedreiras P, Luis A (2003) The flexible time-triggered (FTT) paradigm: an approach to QoS management in distributed real-time systems. In: Proceedings International Parallel and Distributed Processing Symposium, p 9Li H, Sweeney J, Ramamritham K, Grupen R, Shenoy P (2003) Real-time support for mobile robotics. In: The 9th IEEE Real-Time and Embedded Technology and Applications Symposium. Proceedings, pp 10–18Chetto H, Chetto M (1989) Some results of the earliest deadline scheduling algorithm. IEEE Trans Softw Eng 15(10):1261–1269Liu R, Zhang X (2017) Systems of natural-language-facilitated human-robot cooperation: a review. arXiv:1701.08269v2Tsarouchi P, Makris S, Chryssolouris G (2016) Human–robot interaction review and challenges on task planning and programming. Int J Comput Integr Manuf 29(8):916–931Moniz A (2013) Organizational concepts and interaction between humans and robots in industrial environments. In: IEEE-RAS-IARP Joint Workshop on Technical Challenges for Dependable Robots in Human Environment, TokyoMayer MP, Odenthal B, Faber M, Winkelholz C, Schlick CM (2014) Cognitive engineering of automated assembly processes. Hum Factors Ergon Manuf Serv Ind 24(3):348–368Agostini A, Torras C, Wörgötter F (2011) Integrating task planning and interactive learning for robots to work in human environments. In: IJCAIKwon W, Suh I (2014) Planning of proactive behaviors for human–robot cooperative tasks under uncertainty. Knowl Based Syst 72:81–95Chen F, Sekiyama K, Sasaki H, Huang J, Sun B, Fukuda T (2011) Assembly strategy modeling and selection for human and robot coordinated cell assembly. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 4670–4675Gombolay M, Wilcox R, Diaz A, Yu F (2013) Towards successful coordination of human and robotic work using automated scheduling tools: an initial pilot study. In: Proceedings of Robotics: Science and Systems, Human–Robot Collaboration WorkshopGombolay MC, Gutierrez RA, Clarke SG, Sturla GF, Shah JA (2015) Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams. Auton Robots 39(3):293–312Frontoni E, Mancini A, Caponetti F, Zingaretti P (2006) A framework for simulations and tests of mobile robotics tasks. In: 2006 14th Mediterranean Conference on Control and Automation, pp 1–6I. Embarcadero Technologies, C++ Builder 10.2. https://www.embarcadero.com

Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

International audienceThis paper addresses the interest of using Punctual versus Continuous coordination for mobile multi-robot systems where robots use auction sales to allocate tasks between them and to compute their policies in a distributed way. In Continuous coordination, one task at a time is assigned and performed per robot. In Punctual coordination, all the tasks are distributed in Rendezvous phases during the mission execution. However , tasks allocation problem grows exponentially with the number of tasks. The proposed approach consists in two aspects: (1) a control architecture based on topo-logical representation of the environment which reduces the planning complexity and (2) a protocol based on Sequential Simultaneous Auctions (SSA) to coordinate Robots' policies. The policies are individually computed using Markov Decision Processes oriented by several goal-task positions to reach. Experimental results on both real robots and simulation describe an evaluation of the proposed robot architecture coupled wih the SSA protocol. The efficiency of missions' execution is empirically evaluated regarding continuous planning

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Integration of Mobile Robot Navigation on a Control Kernel Middleware based system

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-07593-8_55This paper introduces how a mobile robot can perform navigation tasks by taking the advantages of implementing a control kernel middleware (CKM) based system. Smart resources are also included into the topology of the system for improving the distribution of computational load of the needed tasks. The CKM and the smart resources are both highly recon gurable, even on execution time, and they also implement.lt detection mechanisms and QoS policies. By combining of these capabilities, the system can be dinamically adapted to the requirements of its tasks. Furthermore, this solution is suitable for most type of robots, including those which are provided of a low computational power because of the distribution of load, the bene ts of exploiting the smart resources capabilities, and the dynamic performance of the system.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-002-02.Munera Sánchez, E.; Muñoz Alcobendas, M.; Posadas-Yagüe, J.; Poza-Lujan, J.; Blanes Noguera, F. (2014). Integration of Mobile Robot Navigation on a Control Kernel Middleware based system. En Distributed Computing and Artificial Intelligence, 11th International Conference. Springer Advances in Intelligent Systems and Computing Volume 290. 477-484. https://doi.org/10.1007/978-3-319-07593-8_55S477484Rock (Robot Constrution Toolkit), http://www.rock-robotics.org/Albertos, P., Crespo, A., Simó, J.: Control kernel: A key concept in embedded control systems. In: 4th IFAC Symposium on Mechatronic Systems (2006)Bruyninckx, H., Soetens, P., Koninckx, B.: The Real-Time Motion Control Core of the Orocos Project. In: IEEE International Conference on Robotics and Automation, pp. 2766–2771 (2003)De Souza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 237–267 (2002)Fitzpatrick, P., Metta, G., Natale, L.: Towards long-lived robot genes. Robotics and Autonomous Systems (2008)Mohamed, N., Al-Jaroodi, J., Jawhar, I.: Middleware for robotics: A survey. In: 2008 IEEE Conference on Robotics, Automation and Mechatronics, pp. 736–742. IEEE (2008)Montemerlo, M., Roy, N., Thrun, S.: Perspectives on standardization in mobile robot programming: The carnegie mellon navigation (carmen) toolkit. In: Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 3, pp. 2436–2441. IEEE (2003)Muñoz, M., Munera, E., Blanes, J.F., Simo, J.E., Benet, G.: Event driven middleware for distributed system control. XXXIV Jornadas de Automatica, 8 (2013)Muñoz, M., Munera, E., Blanes, J.F., Simó, J.E.: A hierarchical hybrid architecture for mission-oriented robot control. In: Armada, M.A., Sanfeliu, A., Ferre, M. (eds.) First Iberian Robotics Conference of ROBOT 2013. AISC, vol. 252, pp. 363–380. Springer, Heidelberg (2014)Sánchez, E.M., Alcobendas, M.M., Noguera, J.F.B., Gilabert, G.B., Ten, J.E.S.: A reliability-based particle filter for humanoid robot self-localization in RoboCup Standard Platform League. Sensors (Basel, Switzerland) 13(11), 14954–14983 (2013)Poza-Luján, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E.: Relationship between Quality of Control and Quality of Service in Mobile Robot Navigation. In: Omatu, S., De Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 557–564. Springer, Heidelberg (2012)Proetzsch, M., Luksch, T., Berns, K.: Development of complex robotic systems using the behavior-based control architecture iB2C. Robotics and Autonomous Systems 58(1), 46–67 (2010)Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: An open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3 (2009)Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation-mobile robot navigation with uncertainty in dynamic environments. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation, vol. 1, pp. 35–40. IEEE (1999)Nicolau, V., Muñoz, M., Simó, J.: KertrolBot Platform: SiDiReLi: Distributed System with Limited Resources. Technical report, Institute of Control Systems and Industrial Computing - Polytechnic University of Valencia, Valencia, Spain (2011

MARS: An Educational Environment for Multiagent Robot Simulations

Undergraduate robotics students often find it difficult to design and validate control algorithms for teams of mobile robots. This is mainly due to two reasons. First, very rarely educational laboratories are equipped with large teams of robots, which are usually expensive, bulky and difficult to manage and maintain. Second, robotics simulators often require student to spend much time to learn their use and functionalities. For this purpose, a simulator of multi-agent mobile robots named MARS has been developed within the Matlab environment, with the aim of facilitating students to simulate a wide variety of control algorithms in an easy way and without spending time for understanding a new language. Through this facility, the user is able to simulate multi-robot teams performing different tasks, from cooperative to competitive ones, by using both centralized and distributed controllers. Virtual sensors are provided to simulate real devices. A graphical user interface allows students to monitor the robots behaviour through an online animation

SAVA 3: A testbed for integration and control of visual processes

The development of an experimental test-bed to investigate the integration and control of perception in a continuously operating vision system is described. The test-bed integrates a 12 axis robotic stereo camera head mounted on a mobile robot, dedicated computer boards for real-time image acquisition and processing, and a distributed system for image description. The architecture was designed to: (1) be continuously operating, (2) integrate software contributions from geographically dispersed laboratories, (3) integrate description of the environment with 2D measurements, 3D models, and recognition of objects, (4) capable of supporting diverse experiments in gaze control, visual servoing, navigation, and object surveillance, and (5) dynamically reconfiguarable

A Scalable Distributed Approach to Mobile Robot Vision

This paper documents our progress during the first year of work on our original proposal entitled 'A Scalable Distributed Approach to Mobile Robot Vision'. We are pursuing a strategy for real-time visual identification and tracking of complex objects which does not rely on specialized image-processing hardware. In this system perceptual schemas represent objects as a graph of primitive features. Distributed software agents identify and track these features, using variable-geometry image subwindows of limited size. Active control of imaging parameters and selective processing makes simultaneous real-time tracking of many primitive features tractable. Perceptual schemas operate independently from the tracking of primitive features, so that real-time tracking of a set of image features is not hurt by latency in recognition of the object that those features make up. The architecture allows semantically significant features to be tracked with limited expenditure of computational resources, and allows the visual computation to be distributed across a network of processors. Early experiments are described which demonstrate the usefulness of this formulation, followed by a brief overview of our more recent progress (after the first year)

Supervisory control of fuzzy discrete event systems with applications to mobile robotics

Fuzzy Discrete Event Systems (FDES) were proposed in the literature for modeling and control of a class of event driven and asynchronous dynamical systems that are affected by deterministic uncertainties and vagueness on their representations. In contrast to classical crisp Discrete Event Systems (DES), which have been explored to a sufficient extent in the past, an in-depth study of FDES is yet to be performed, and their feasible real-time application areas need to be further identified. This research work intends to address the supervisory control problem of FDES broadly, while formulating new knowledge in the area. Moreover, it examines the possible applications of these developments in the behavior-based mobile robotics domain. An FDES-based supervisory control framework to facilitate the behavior-based control of a mobile robot is developed at first. The proposed approach is modular in nature and supports behavior integration without making state explosion. Then, this architecture is implemented in simulation as well as in real-time on a mobile robot moving in unstructured environments, and the feasibility of the approach is validated. A general decentralized supervisory control theory of FDES is then established for better information association and ambiguity management in large-scale and distributed systems, while providing less complexity of control computation. Furthermore, using the proposed architecture, simulation and real-time experiments of a tightly-coupled multi-robot object manipulation task are performed. The results are compared with centralized FDES-based and decentralized DES-based approaches. -- A decentralized modular supervisory control theory of FDES is then established for complex systems having a number of modules that are concurrently operating and also containing multiple interactions. -- Finally, a hierarchical supervisory control theory of FDES is established to resolve the control complexity of a large-scale compound system by modularizing the system vertically and assigning multi-level supervisor hierarchies. As a proof-of-concept example to the established theory, a mobile robot navigation problem is discussed. This research work will contribute to the literature by developing novel knowledge and related theories in the areas of decentralized, modular and hierarchical supervisory control of FDES. It also investigates the applicability of these contributions in the mobile robotics arena