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As for Deep Learning method, a 10-layer Convolutional Neural Network (CNN) is designed which comes to a high recognition accuracy of 97 percent in Tensorflow and success rate of obstacle avoidance is over 90 percent. With regard to liDAR-based Image Processing approach, decision is made by a special method of counting the number of Point Cloud Data (PCD) which is generated by 2D liDAR and a success rate over 90 percent is achieved as well. When two kinds of methods work together, a robust success rate of 100 percent is realized. Meanwhile, Inertial Measurement Unit (IMU) and Xbox360 are taken into consideration for Pose Estimation and Data Collection. Finally, all functions are integrated in Robot Operation System (ROS) on platform of nVidia Jetson TX1.<\/jats:p>","DOI":"10.3233\/jifs-169706","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T13:26:29Z","timestamp":1529069189000},"page":"1695-1705","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Design and implementation of a novel obstacle avoidance scheme based on combination of CNN-based deep learning method and liDAR-based image processing approach"],"prefix":"10.1177","volume":"35","author":[{"given":"Chengmin","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, China"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, China"}]},{"given":"Wen","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang, China"}]},{"given":"Cao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, China"}]},{"given":"Yihuai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, China"}]}],"member":"179","published-online":{"date-parts":[[2018,6,13]]},"reference":[{"issue":"3","key":"e_1_3_2_2_2","first-page":"469","article-title":"A robot manipulator with adaptive fuzzy controller in obstacle avoidance","volume":"97","author":"Sreekumar M.","year":"2016","unstructured":"SreekumarM., A robot manipulator with adaptive fuzzy controller in obstacle avoidance, Journal of the Institution of Engineers97(3) (2016), 469\u2013478.","journal-title":"Journal of the Institution of Engineers"},{"key":"e_1_3_2_3_2","first-page":"1","article-title":"Reactive obstacle avoidance for highly maneuverable vehicles based on a two-stage optical flow clustering","volume":"99","author":"Schaub A.","year":"2017","unstructured":"SchaubA., BaumgartnerD. and BurschkaD., Reactive obstacle avoidance for highly maneuverable vehicles based on a two-stage optical flow clustering, IEEE Transactions on Intelligent Transportation Systems99 (2017), 1\u201316.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574714000460"},{"key":"e_1_3_2_5_2","first-page":"1","article-title":"A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance","author":"Yen C.T.","year":"2016","unstructured":"YenC.T. and ChengM.F., A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance, Microsystem Technologies (2016), 1\u201311.","journal-title":"Microsystem Technologies"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"ChiangS.Y. 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