Customized mobile robots are commonplace in manufacturing and material handling applications. One class of mobile robots, known as Automatic Guided Vehicles (AGV), follow a fixed path along the floor using tracks, RFID tags, or magnetic tape. These robots typically travel along predetermined routes and offer limited flexibility for changes to be made or for their use in environments like hospitals or military installations. Moving away from traditional fixed AGV systems to wireless and dynamic control and monitoring presents some distinct advantages and new opportunities. This type of robot is known as an Autonomous AGV. A prototype smart materials warehouse is presented in this paper as a platform to explore some particular aspects of this technology. First, four multi-purpose mobile robots were built using off-the-shelf BattleBot kits, wireless Arduino controls, and fabricated components. Secondly, a Microsoft Kinect camera was installed on one robot for visual feedback, obstacle avoidance, and shape recognition. Lastly, a ceiling mounted IP camera was used with software developed using Visual Studio .NET and the C# wrapper for OpenCV (EmguCV) to facilitate robot path development, video processing and real-time tracking. Testing of the completed system was done in a 2000 sq. ft. mock warehouse set up with stations for shipping/receiving, storage, staging areas, and processes including cutting, milling, and turning for preparing raw stock to be used in production. As cyber-physical systems research continues to grow, the integration of computational algorithms, physical systems, wireless controls, and custom user interfaces will undoubtedly lead to their increased use throughout society. This work was completed as part of the Northwest Manufacturing Initiative at the Oregon Institute of Technology. Possibilities for applying the results of this work in the military, retail and service sectors are also identified. All hardware and software for the project was developed to facilitate future work.
[1]
Hari Balakrishnan,et al.
Tracking moving devices with the cricket location system
,
2004,
MobiSys '04.
[2]
S. Reveliotis.
Conflict resolution in AGV systems
,
2000
.
[3]
Gaurav S. Sukhatme,et al.
People tracking and following with mobile robot using an omnidirectional camera and a laser
,
2006,
Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..
[4]
Alex Fukunaga,et al.
Cooperative mobile robotics: antecedents and directions
,
1995
.
[5]
Jeong Ho Lee,et al.
Bridge inspection robot system with machine vision
,
2009
.
[6]
Stefan Wermter,et al.
A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera
,
2011,
J. Ambient Intell. Smart Environ..
[7]
Roy Sterritt,et al.
The Utility of Robot Sensory Devices in a Collaborative Autonomic Environment
,
2014
.
[8]
Sameer Aryal.
Integrating Camera Recognition and RFID System for Assets Tracking and Warehouse Management
,
2012
.
[9]
Hobart R. Everett,et al.
Where am I?" sensors and methods for mobile robot positioning
,
1996
.
[10]
Tetsuya Yagi,et al.
A Robot Vision System for Collision Avoidance Using a Bio-inspired Algorithm
,
2007,
ICONIP.
[11]
Yael Edan,et al.
Evaluation of automatic guided vehicle systems
,
2009
.
[12]
Emanuele Frontoni,et al.
A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks
,
2010,
J. Intell. Robotic Syst..