Omni-directional stepping robotic system based on cellular grid map

doi:10.7523/j.issn.1002-1175.2013.03.018

Abstract

Abstract:

We design and implement an omni-directional stepping robotic system based on cellular grid map to provide physical services in intelligent space. By analyzing and modeling the mobile platform in mechanics, kinematics, and dynamics, we design the robot suitable on cellular grid map, with the help of high-precision optical odometer based on FPGA and of laser calibration device. Based on multi-dimensional sensor data fusion, we make the path planning task into two stages: six-directional local path planning and global path planning based on TSP optimization of genetic algorithm. Experiments show the accuracy and robustness of the system during the actual operation.

Key words: intelligent space, stepping robot, data fusion, cellular grid map, path planning

CLC Number:

TP24

YU Chong. Omni-directional stepping robotic system based on cellular grid map[J]. Journal of University of Chinese Academy of Sciences, 2013, 30(3): 394-402.

References

[1] Reetz Eike S,Tonjes Ralf,Baker Nigel. Towards global smart spaces: Merge wireless sensor networks into context-aware systems[C]//5th IEEE International Symposium on Wireless Pervasive Computing (ISWPC). Italy:Modena,2010:337-342.

[2] Blum N,Yamada J,Fukayama A,et al. A smart information sharing architecture in a multi-access network, multi-service environment[C]//International Conference on Communications (ICC). Japan:Kyoto,2011:1-6.

[3] Varkonyi-Koczy A R,Tusor B. Human-computer interaction for smart environment applications using fuzzy hand posture and gesture models[J]. IEEE Transactions on Instrumentation and Measurement,2011,60(5):1505-1514.

[4] Ruiz D,Urena J,Garcia J C,et al. Simultaneous mobile robot positioning and LPS self-calibration in a smart space[C]//International Symposium on Industrial Electronics (ISIE). Spain:Henares,2010:2865-2870.

[5] Anousaki G C,Kyriakopoulos K J. Simultaneous localization and map building of skid-steered robots[J]. IEEE Robotics & Automation Magazine,2007,14(1):79-89.

[6] Merhy B A,Payeur P,Petriu E M. Application of segmented 2-D probabilistic occupancy maps for robot sensing and navigation[J]. IEEE Transactions on Instrumentation and Measurement,2008,57(12):2827-2837.

[7] Olivier K, Francois C. Multi-sensor data fusion in sensor-based control: Application to multi-camera visual servoing[C]//International Conference on Robotics and Automation (ICRA). France:Beaulieu,2011:4518-4523.

[8] Yu C,Gao X,Qiu Q W. Design and implementation of robot odometer based on multi-sensor data fusion[J]. Chinese Journal of Sensors and Actuators,2012,25(1):53-57(in Chinese). 余翀,高翔,邱其文. 基于多传感器数据融合的机器人里程计设计与实现[J]. 传感技术学报,2012,25(1):53-57.

[9] Xu S S,Lin X G. Asynchronous multi-sensor hierarchical adaptive data fusion algorithm[C]//International Conference on Complex Medical Engineering (CME). China:Harbin,2011:285-288.

[10] Jemin G, Lancem K. Multi-sensor data fusion: An unscented least squares approach[C]//Proceedings of the 14th International Conference on Information Fusion (FUSION). USA:Chicago,2011:1-8.

[11] Maarefdoust R,Amani M. Risk reduction in autonomous local path-planning for a mobile robot with set fitness function[C]//3rd International Conference on Computer Research and Development (ICCRD). China:Shanghai,2011:195-198.

[12] Lee S K, Lee S, Nam C J,et al. Local path planning scheme for car-like vehicles shortest turning motion using geometric analysis[C]//International Conference on Intelligent Robots and Systems (IROS). South Korea:Hawolgok,2010:4761-4768.

[13] Tsai C C,Huang H C,Chan C K. Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation[J]. IEEE Transactions on Industrial Electronics,2011,58(10):4813-4821.

[14] Liu Y J,Wang H Y, Xie J. Global path planning method based on geometry algorithm in a strait environment[C]//International Conference on Electrical and Control Engineering (ICECE). China:Wuhan,2010:1066-1069.