International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019 3902 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: D8324118419/2019©BEIESP DOI:10.35940/ijrte.D8324.118419 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 r x =.975, n x =6, p x =0.001 and r y =.987, n y =6, p y =0.000 and path tracking time of 8.47s. Index Terms: Path Planning, Mobile Waste Robotics, Artificial Neural Networks, Levenberg-Marquardt, Scaled Conjugate Gradient I. INTRODUCTION One of the most challenging competencies in the design of a mobile robot is autonomous navigation, which is a process model used to reach a specific goal while avoiding obstacles. Shown on Fig. 1 is the autonomous navigation process composed of four fundamental sub-processes [1]. Initially, the robot must perceive its environment to extract meaningful data through the utilization of sensors. From these data, the robot should be able to navigate and locate its position in the environment. From then, the robot should be able to cognize and plan its path in order to achieve its goal. Lastly, it should be able to control and modulate its motions to achieve the desired trajectory while simultaneously avoiding obstacles [2]. Among these four sub-processes of autonomous navigation, path planning is considered to be the most common issue particularly in unstructured environment. Such issues are likewise practically true in the case of mobile robots Revised Manuscript Received on November 15, 2019 Ralph Sherwin A. Corpuz, Ph.D., Director of Quality Assurance and Assistant Professor, Electronics Engineering Technology, Technological University of the Philippines, Manila, Philippines. used for waste disposal purposes. In path planning, a mobile robot aims to navigate from a start point to an end point, also known as target or goal, and maintains a collision-free trajectory. Some of the strategies sought toward successful path planning are not limited to shortest distance, smoothness of path, minimum energy consumption or a combination thereof such as shortest distance with minimal possible time, among others [3, 4]. Fig. 1.Autonomous Navigation Process To overcome issues associated with path planning, researchers explored various classical and heuristic methods. Some of the popular classical methods include cell decomposition, potential field, sampling-based, subgoal network and probabilistic roadmap. These classical methods are preferred for real-time motion planning applications because they are easy to implement, and modestly obtain good results [2, 3]. On the other hand, however, these classical methods do not produce optimal paths and are unstable in dynamic environments with multiple obstacles [2, 5]. In order to overcome the inefficiencies of classical methods, heuristic methods are employed for this purpose. Some of the popular heuristic methods used in mobile waste-robots include Artificial Neural Networks (ANN) [6], Fuzzy Logic Control (FLC) [7], or hybrid algorithms [8] etc. Of all these heuristic methods, ANNs are among the most recently-explored. ANN is a tool that acts like a human brain by learning and modelling complex relationships between input and output data [9] in order to achieve autonomous navigation. They are found to be efficient in terms of nonlinear mapping, learning ability and parallel processing, hence, they are highly recommended for heuristic methods of path planning [2]. On the other hand, however, ANNs are time-consuming, impractical for simple logic functions, and do not guarantee optimal solutions. With that said, the extent of optimization applicability and accuracy of ANNs still remain an opportunity for further study. The motivation of this paper focuses in characterizing the path planning capabilities of a mobile-waste robot in order to ensure navigational accuracy and path tracking time through the use of Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning Using Artificial Neural Networks Ralph Sherwin A. Corpuz