State-of-the-Art Review of Autonomous Intelligent Vehicles (AIV) Technologies for the Automotive and Manufacturing Industry

Research in Autonomous Intelligent Vehicles (AIV) has been done for the past 25 years and is continuously creating advancements and capabilities. AIVs will out-strip Automated Guided Vehicles (AGV) as leaders of material handling equipment. It is the ability of AIVs to operate remotely and safely with repeatability at the request and demand of the Manufacturing Execution System (MES). Integration of AIV technologies will increase productivity through consistent and seamless transportation of product in current manufacturing environments. AIV technology removes the mundane laborious manual operations from the human operator, offering workers the ability to work in a more meaningful role within the production line. Increased manufacturing output will ultimately benefit the economy and job security. This review paper will examine AIV technologies for advanced flexible manufacturing systems to improve manufacturing processes. The removal of conveyors and the inclusion of AIVs will promote flexibility within the factory floor and increase the realisation of Industry 4.0

[1]  Selma Sabanovic,et al.  George Charles Devol, Jr. [History] , 2012, IEEE Robotics & Automation Magazine.

[2]  Woo-Jin Seo,et al.  A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding , 2011 .

[3]  Grzegorz Granosik,et al.  USING ROBOT OPERATING SYSTEM FOR AUTONOMOUS CONTROL OF ROBOTS IN EUROBOT, ERC AND ROBOTOUR COMPETITIONS , 2016 .

[4]  Suresh Garg,et al.  Automated guided vehicle configurations in flexible manufacturing systems: a comparative study , 2015 .

[5]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[6]  Jianzhong Li,et al.  A generic data analytics system for manufacturing production , 2018, Big Data Min. Anal..

[7]  Jens-Steffen Gutmann,et al.  Markov-Kalman localization for mobile robots , 2002, Object recognition supported by user interaction for service robots.

[8]  Seoyong Shin,et al.  Mobile robot navigation with distance control , 2012, 2012 International Conference of Robotics and Artificial Intelligence.

[9]  Camilla Feledy,et al.  A State of the Art Map of the AGVS Technology and a Guideline for How and Where to Use It , 2017 .

[10]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[11]  Michael Ruderman,et al.  Guest Editorial Special Section on Recent Trends and Developments in Industry 4.0 Motivated Robotic Solutions , 2018, IEEE Trans. Ind. Informatics.

[12]  Dan Wu,et al.  A dynamic size MCL algorithm for mobile robot localization , 2010, 2010 IEEE International Conference on Robotics and Biomimetics.

[13]  Arshad Javed,et al.  ROS based service robot platform , 2018, 2018 4th International Conference on Control, Automation and Robotics (ICCAR).

[14]  Lentin Joseph Mastering ROS for robotics programming : design, build, and simulate complex robots using robot operating system and master its out-of-the-box functionalities , 2015 .

[15]  Andrey V. Savkin,et al.  Wireless Sensor Network Based Navigation of Micro Flying Robots in the Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[16]  Sun Lei,et al.  A ROS-based smooth motion planning scheme for a home service robot , 2015, 2015 34th Chinese Control Conference (CCC).

[17]  Lentin Joseph Robot Operating System (ROS) for Absolute Beginners , 2018, Apress.

[18]  Luiz M. G. Goncalves,et al.  RoboServ: A ROS Based Approach towards Providing Heterogeneous Robots as a Service , 2016, 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR).

[19]  Peter Hubinský,et al.  Intelligent Vehicles as the Robotic Applications , 2012 .

[20]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .