Localization of an omnidirectional transport robot using IEEE 802.15.4a ranging and laser range finder

Automated Guided Vehicles (AGVs) are used in warehouses, distribution centers and manufacturing plants in order to automate the internal material flow. Usually AGVs are designed to transport large and heavy transport units such as Euro-pallets or mesh pallets. Just-in-time inventory management and lean production requires small transportation units to enable one-piece-flow. Furthermore short production cycles require a flexible material flow which can not be fulfilled by continuous material handling devices like belt or roll conveyors. A solution to meet these demands are small mobile robots for material transport which can replace conventional conveyor systems or large AGVs. The paper presents localization and tracking of an omnidirectional mobile robot equipped with Mecanum wheels, which was designed to transport Euro-bins in a distribution center or warehouse. Localization is realized by sensor fusion of range measurements obtained from an IEEE 802.15.4a network and laser range finders. The IEEE 802.15.4a network is used for communication as well as for global localization. Laser range finders are used to detect landmarks and to provide accurate positioning for docking maneuvers. The range measurements are fused in a Monte Carlo Particle Filter. The paper develops a new motion model for an omnidirectional robot as well as a sensor model for IEEE 802.15.4a range measurements. The experimental results presented in the paper show the effectiveness of the developed models.

[1]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[2]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[3]  Ha Yoon Song,et al.  Multilevel localization for Mobile Sensor Network platforms , 2008, 2008 International Multiconference on Computer Science and Information Technology.

[4]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[5]  Eric Guizzo,et al.  Three Engineers, Hundreds of Robots, One Warehouse , 2008, IEEE Spectrum.

[6]  Charles P. Neuman,et al.  Modeling and control of wheeled mobile robots , 1988 .

[7]  Li-Chen Fu,et al.  An Efficient Hierarchical Localization for Indoor Mobile Robot with Wireless Sensor and Pre-Constructed Map , 2008 .

[8]  Paul J.M. Havinga,et al.  Wireless Sensor Networks and Beyond: A Case Study on Transport and Logistics , 2005 .

[9]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[10]  Cipriano Galindo,et al.  Application of UWB and GPS technologies for vehicle localization in combined indoor-outdoor environments , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[11]  Martin Vossiek,et al.  Wireless local positioning , 2003 .

[12]  Christof Röhrig,et al.  Indoor location tracking in non-line-of-sight environments using a IEEE 802.15.4a wireless network , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Kai Furmans,et al.  A Case for Material Handling Systems, Specialized on Handling Small Quantities , 2008 .

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

[15]  Christof Röhrig,et al.  Tracking of transport vehicles for warehouse management using a wireless sensor network , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.