Discrete Event Behavior-Based Distributed Architecture Design for Autonomous Intelligent Control of Mobile Robots with Embedded Petri Nets

This chapter presents a design methodology of discrete event distributed control architecture for autonomous mobile robot systems. A modular, behavior-based distributed software architecture is presented on a hierarchical distributed microcontroller based hardware structure for intelligent control of mobile robots. Some intelligent behaviors, such as wall following, obstacle rounding, target seeking, and local environment mapping, have been implemented using sensor control modules such as multiple infrared range finding sensor modules and motion control modules to detect walls and obstacles in the surroundings of a mobile robot, based on environment features such as lines and corners estimated using a set of range sensors and a vision sensor. Upon these behavior modules, a Petri net based approach was applied to coordination of several concurrent activities of modules for the high-level tasks such as sensory navigation in unknown environments. Task specification implies the definition of a control program composed of behavior commands, which are not expressed in a sequential fashion but implicating parallel processing control. The net model can be directly obtained from the system requirements specification of each particular application. Thus, the remaining levels of the control structure are common to a wide range of applications. The Petri net based approach validates the implementation of synchronization and coordination in discrete event behavior-based control. Behavior modules are composed to design more complex modules according to applications. The detailed function of each control module is specialized according to the application, so that new control strategies can be easily embedded in the control modules for real-time performance of robotic actions. Compared to hand–written coding in robot program, because of explicit representation of robotic actions, behaviors and tasks, the system design procedure facilitates the understanding of the interaction among the different processes that might be present in the mobile robot control system. Consequently, it is easy and computationally inexpensive to design, write, and debug planned tasks. Besides it is possible to verify structural and behavioral properties of these programs owing to formal specification.

[1]  Pedro U. Lima,et al.  Petri net models of robotic tasks , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[2]  Erann Gat,et al.  Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots , 1992, AAAI.

[3]  Sundarapandian Vaidyanathan,et al.  Chaos Modeling and Control Systems Design , 2014, Chaos Modeling and Control Systems Design.

[4]  Luis Montano,et al.  Using the Time Petri Net Formalism for Specification, Validation, and Code Generation in Robot-Control Applications , 2000, Int. J. Robotics Res..

[5]  Quanmin Zhu,et al.  Complex System Modelling and Control Through Intelligent Soft Computations , 2016, Studies in Fuzziness and Soft Computing.

[6]  Gen'ichi Yasuda,et al.  Construction of infrared wireless inter-robot communication networks for distributed sensing and coo , 2002 .

[7]  Keith L. Doty,et al.  Swarm robot materials handling paradigm for a manufacturing workcell , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[8]  Paul Freedman,et al.  Time, Petri nets, and robotics , 1991, IEEE Trans. Robotics Autom..

[9]  James Lyle Peterson,et al.  Petri net theory and the modeling of systems , 1981 .

[10]  Ronald C. Arkin,et al.  Motor schema based navigation for a mobile robot: An approach to programming by behavior , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[11]  Quanmin Zhu,et al.  Advances and Applications in Sliding Mode Control Systems , 2014, Advances and Applications in Sliding Mode Control Systems.

[12]  G. Yasuda,et al.  Sensor-based path planning and intelligent steering control of nonholonomic mobile robots , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[13]  Wolfgang A. Halang,et al.  Design of microprogrammed controllers to be implemented in FPGAs , 2011, Int. J. Appl. Math. Comput. Sci..

[14]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[15]  Pattie Maes,et al.  Situated agents can have goals , 1990, Robotics Auton. Syst..

[16]  GianAntonio Magnani,et al.  A technique for designing robotic control systems based on Petri nets , 1998, IEEE Trans. Control. Syst. Technol..

[17]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[18]  Matthieu Herrb,et al.  Design of a modular architecture for autonomous robot , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[19]  Sundarapandian Vaidyanathan,et al.  Computational Intelligence Applications in Modeling and Control , 2015, Computational Intelligence Applications in Modeling and Control.

[20]  Elizabeth R. Stuck,et al.  Using a Blackboard to Integrate Multiple Activities and Achieve Strategic Reasoning for Mobile-Robot Navigation , 1995, IEEE Expert.

[21]  Massimo Caccia,et al.  Execution control of robotic tasks : a Petri net-based approach , 2005 .

[22]  N. A. Duffie Heterarchical control of highly distributed manufacturing systems , 1996 .

[23]  Rodney A. Brooks,et al.  Asynchronous Distributed Control System For A Mobile Robot , 1987, Other Conferences.

[24]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[25]  Jonathan H. Connell,et al.  SSS: a hybrid architecture applied to robot navigation , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.