"Brains" for Robots: Application of the Mivar Expert Systems for Implementation of Autonomous Intelligent Robots

Abstract Recently the contemporary robotic systems can manipulate different objects and make decisions in a range of situations due to significant advances in innovation technologies and artificial intelligence. The new expert technologies can handle millions of instructions on computers and smartphones, which allow them to be used as a tool to create “decision-making systems” for autonomous robots. The goal of this paper was to create a dynamic algorithm of robot actions that can be used in the decision module has been considered. It is proposed to use Mivar expert systems of a new generation for high-level control. The experiment results showed that Mivar decision-making systems can control groups of small robots and even an unmanned autonomous car in real time. The algorithms created in the Mivar environment can be very flexible, and their build-up depends only on engineering approaches. In addition to traditional low-level robot control systems, a Mivar decision-making system has been implemented, which can be considered as universal “Brains” for autonomous intelligent robots and now knowledge bases can be created and various robots can be trained for practical tasks.

[1]  Gi Hyun Lim,et al.  Shared representations of actions for alternative suggestion with incomplete information , 2019, Robotics Auton. Syst..

[2]  Yisheng Guan,et al.  Real Time Obstacle Avoidance and Navigation with Mobile Robot via Local Elevation Information , 2019, 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[3]  Jeremy L. Rickli,et al.  A Framework for Collaborative Robot (CoBot) Integration in Advanced Manufacturing Systems , 2016 .

[4]  Oleg Olegovich Varlamov Wi!Mi Expert System Shell as the Novel Tool for Building Knowledge-Based Systems with Linear Computational Complexity , 2018 .

[5]  Moritz Tenorth,et al.  KnowRob: A knowledge processing infrastructure for cognition-enabled robots , 2013, Int. J. Robotics Res..

[6]  Ellips Masehian,et al.  Modular robotic systems: Methods and algorithms for abstraction, planning, control, and synchronization , 2015, Artif. Intell..

[7]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[8]  Tong Jia,et al.  Visual perception and navigation of security robot based on deep learning , 2020, 2020 IEEE International Conference on Mechatronics and Automation (ICMA).

[9]  Alessandro Saffiotti,et al.  Geometric backtracking for combined task and motion planning in robotic systems , 2017, Artif. Intell..

[10]  Peter Stone,et al.  A synthesis of automated planning and reinforcement learning for efficient, robust decision-making , 2016, Artif. Intell..

[11]  Shaohan Hu,et al.  DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing , 2016, WWW.

[12]  Oleg O. Varlamov MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and Practical Realization of Automated Constructor of Algorithms Handling More than Three Million Production Rules , 2011, ArXiv.

[13]  Stefano Stalio,et al.  U-LITE, a Private Cloud Approach for Particle Physics Computing , 2019, Int. J. Cloud Appl. Comput..

[14]  Andrey Vladimirovich Ostroukh,et al.  Automated process control system of mobile crushing and screening plant , 2018 .

[15]  Mikhail Pavlovich Bulat,et al.  The History of the Gas Bearings Theory Development , 2013 .

[16]  Lennart Svensson,et al.  LIDAR-based driving path generation using fully convolutional neural networks , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[17]  A. Ospanov,et al.  Mathematical model of high-temperature tube-shaped pasta drying in a conveyer belt drier , 2020 .

[18]  D V Aladin,et al.  Creation of autonomous groups of combine harvesters and tractors for agriculture based on the Mivar decision-making systems “ROBO!RAZUM” , 2020 .

[19]  Vedula Venkateswara Rao,et al.  Performance of Memory Virtualization Using Global Memory Resource Balancing , 2019, Int. J. Cloud Appl. Comput..

[20]  Oleg Olegovich Varlamov,et al.  Experimental Autonomous Road Vehicle with Logical Artificial Intelligence , 2017 .

[21]  M. Beetz,et al.  Movement-aware action control — Integrating symbolic and control-theoretic action execution , 2012, 2012 IEEE International Conference on Robotics and Automation.

[22]  Vijay S. Rajpurohit,et al.  Fast and Efficient Multiview Access Control Mechanism for Cloud Based Agriculture Storage Management System , 2019, Int. J. Cloud Appl. Comput..

[23]  M. Khairudin,et al.  VISION-BASED MOBILE ROBOT NAVIGATION FOR SUSPICIOUS OBJECT MONITORING IN UNKNOWN ENVIRONMENTS , 2020 .

[24]  Fiorenzo Franceschini,et al.  A conceptual framework to evaluate human-robot collaboration , 2020, The International Journal of Advanced Manufacturing Technology.

[25]  Alessandro V. Papadopoulos,et al.  Towards Reactive Robot Applications in Dynamic Environments , 2019, 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

[26]  Bodo Urban,et al.  An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker-Cobot Agile Manufacturing , 2017, Future Internet.

[27]  D V Aladin,et al.  Control of vehicles and robots: creating of knowledge bases for mivar decision making systems robots and vehicles , 2020 .

[28]  Subramaniam Parasuraman Sensor Fusion for Mobile Robot navigation: Fuzzy Associative Memory , 2012 .

[29]  Carme Torras,et al.  Efficient interactive decision-making framework for robotic applications , 2017, Artif. Intell..

[30]  Weidong Li,et al.  Cobot programming for collaborative industrial tasks: An overview , 2019, Robotics Auton. Syst..

[31]  A. V. Baldin,et al.  Mivar models of reconstruction and expertise of emergency events of road accidents , 2019 .