Autonomous Parallel Parking of Four Wheeled Vehicles Utilizing Adoptive Fuzzy – Neuro Control System Jerome T. Marasigan, Iara Marie B. Saberon, Dan Patrick B. San Jose, Paul Anthony T. Sevilla Argel A. Bandala Department of Electronics and Communications Engineering De La Salle University, Manila Abstract — The study presents an autonomous sensor based parallel parking maneuver on a car-like mobile robot. This project focuses on parallel parking the car-like mobile robot within a given scenario following the fifth degree polynomial reference path in a backward maneuver. Training data, gathered from the fifth degree polynomial path, is subjected to subtractive clustering algorithm to determine the fuzzy controller and trained by the adaptive neuro- fuzzy inference system. The project uses eight ultrasonic sensors, placed strategically to avoid radial imprecision, to detect the obstacles along its path; an accelerometer is also used to detect the inclination of the car-like mobile robot (CLMR). The sensors acquire necessary sensor data for the Neuro-Fuzzy Inference System to determine the proper motion direction at each sampling point. The efficiency of the proposed Neuro-Fuzzy Controller (NFC) design is revealed through the actual results. Keywords - Neuro - Fuzzy, Autonomous Parallel Parking, Adoptive Network I. INTRODUCTION Drivers, in general, find it hard to plan a precise path on parallel parking. Drivers do not have precise measurements in order to control their parking motions. Nonetheless, they still become skillful at executing complicated tasks through experience, common sense and training in pursuing imprecise rules that can be translated in the form of “If…Then…”. These “If…Then…” rules are the linguistic notions behind Fuzzy Logic. Parking a vehicle in parallel requires the driver to learn gradually over time from constant external stimuli exposure and generalization. This process is a kind of neural network wherein the mathematical models are derived from the driver’s parallel parking strategy. Parallel parking problem is generally developing a robust path planning wherein a vehicle must approach a vacant area and park into the area without difficulty. Many studies have been proposed to solve this problem giving sufficient paths consequent from path tracking techniques. Many studies about automated parallel parking are mainly motion planning wherein no collisions are met between the paths from initial position to goal position while satisfying the non holonomic constraints. By integrating the use of Neuro-Fuzzy System and the 5th order polynomial as reference path this study will satisfy these non holonomic constraints. This paper aims to develop an automated parallel parking strategy for a Car-Like- Mobile Robot (CLMR) using Neuro-Fuzzy Logic Control (NFLC) design with the aid of proximity sensors and accelerometer at 4 major parking conditions: sandwiched between two obstacles at level and inclined state (Scenarios 1 & 2 and Scenarios 3 & 4), behind an obstacle (Scenarios 5 & 6) and in front of an obstacle (Scenarios 7 & 8) coming from either right or left. The architecture projected in this study is an Adaptive Neuro-Fuzzy Inference System (ANFIS) structure, which is essentially a fuzzy inference system subjected into the framework of adaptive neural network. Sensor based approach is used for the parallel parking architecture, which is performing the parallel parking maneuver by solely depending on sonar data. II. KINEMATIC EQUATIONS Figure 1. Dynamics and Angles Involved in the Vehicle 2014 IEEE Region 10 Symposium 978-1-4799-2027-3/14/$31.00 ©2014 IEEE 640