An Improved Navigation Method for Robot in Indoor Dynamic Environment Based on Ground Extraction
Main Article Content
Abstract
This paper presents a ground-based navigation method for an indoor robot to reach a predetermined target. By mining depth maps effectively, a mobile robot could find right path and self-locate with a proposed algorithm named Always Move Straight to the Destination (AMDS). The proposed navigation system extracts the ground plane from the depth map provided by an RGB-D camera. Then the navigation system has established an optimal obstacle avoiding strategy with a success rate of 98.7% which is better than some recent comparison methods based on Artificial Neural Network (ANN) classifiers or method of combination of two algorithm of Dynamic Window Approach (DWA) and Anytime Repairing A* (ARA*). The robot’s navigational capability is more flexible than the comparison methods because the angle of direction adjustment is 1 degree. The proposed ground-based navigation method could be integrated into low cost robots.
Keywords
Depth map, Ground plane, Navigation, Path direction
Article Details
References
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[3] Correa, Diogo Santos Ortiz, et al, Mobile robots navigation in indoor environments using kinect sensor, Critical Embedded Systems (CBSEC), Second Brazilian Conference on IEEE (2012) 36-41.
[4] Z. Yong-guo, C. Wei and L. Guang-liang, The Navigation of Mobile Robot Based on Stereo Vision, Fifth International Conference on Intelligent Computation Technology and Automation, Zhangjiajie, Hunan (2012) 670-673.
[5] Biswas, Joydeep, and Manuela Veloso, Depth camera based indoor mobile robot localization and navigation, Robotics and Automation (ICRA), IEEE International Conference on. IEEE (2012) 1697-1702.
[6] László Somlyai, Mobile robot localization using RGB-D camera, ICCC 2013, IEEE 9th International Conference on Computational Cybernetics, Hungary (2013) 131-136.
[7] N.A. Zainuddin, Y.M. Mustafah, Y.A.M. Shawgi, N.K.A.M. Rashid, Autonomous Navigation for Mobile Robot Using Kinect Sensor, 5th International Conference on Computer & Communication Engineering (2014) 28-31.
[8] Yiqun Fu, Guolai Jiang, Wei Feng, Yimin Zhou and Yongsheng Ou, On Real-Time Obstacle Avoidance Using 3-D Point Clouds, Proceedings of the 2014 IEEE, International Conference on Robotics and Biomimetics, Indonesia (2014) 631-636.
[9] Khalid N. Al-Mutib, Ebrahim A. Mattar, Mansour M. Alsulaiman, and H. Ramdane, Stereo Vision SLAM Based Indoor Autonomous Mobile Robot Navigation, Proceedings of the 2014 IEEE, International Conference on Robotics and Biomimetics, Indonesia (2014) 1584- 1589.
[10] Chun C. Lai, Kuo L. Su, Development of an intelligent mobile robot localization system using Kinect RGB-D mapping and neural network, Computers & Electrical Engineering, 67 (2018) 620-628.
[11] Gaoqiang Yang, Fucai Chen, Wen Chen, Mu Fang, Yun-Hui Liu, and Luyang Li, “A New Algorithm for Obstacle Segmentation in Dynamic Environments Using a RGB-D Sensor”, Proceedings of The 2016 IEEE, International Conference on Real-time Computing and Robotics, Cambodia (2016) 374-378.
[12] XIN Jing, JIAO Xiao-liang, YANG Yin, LIU Ding, “Visual Navigation for Mobile Robot with Kinect Camera in Dynamic Environment,” Proceedings of the 35th Chinese Control Conference, China (2016) 4757-4764.