Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning Using Artificial Neural Networks
Ralph Sherwin A. Corpuz
Ralph Sherwin A. Corpuz, Ph.D., Director of Quality Assurance and Assistant Professor, Electronics Engineering Technology, Technological University of the Philippines, Manila, Philippines.
Manuscript received on November 10, 2019. | Revised Manuscript received on November 17, 2019. | Manuscript published on 30 November, 2019. | PP: 3902-3910 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8324118419/2019©BEIESP | DOI: 10.35940/ijrte.D8324.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.
Keywords: Path Planning, Mobile Waste Robotics, Artificial Neural Networks, Levenberg-Marquardt, Scaled Conjugate Gradient.
Scope of the Article: Mobile Applications and Services for IoT.