Analisis Kinerja Perilaku Mobile Robot Penghindar Halangan dengan Fungsi Keanggotaan Non Linear pada Kendali Logika Fuzzy Sugeno

Mobile robot banyak diaplikasikan pada berbagai aspek kehidupan. Navigasi robot merupakan salah satu sistem yang mampu melakukan navigasi yang terdiri dari aktivitas pergerakan seperti menghindari halangan (obstacle avoidance). Navigasi robot mencakup berbagai aktivitas yang saling terkait seperti aktuasi, persepsi dan eksplorasi. Penentuan navigasi yang baik menjadikan robot dapat melakukan eksplorasi yang bebas dari tabrakan dengan penghalang atau robot lain. Penelitian ini dikembangkan dengan menggunakan metode kendali logika Fuzzy dengan fungsi keanggotaan non linear, karena metode logika Fuzzy memiliki kemampuan untuk lebih merepresentasikan dunia nyata. Penelitian ini menghasilkan perancangan model kendali logika Fuzzy dan kemudian diterapkan pada suatu aplikasi perangkat lunak yang dapat mengendalikan robot hingga sukses menghindari halangan dengan baik dalam lingkungan virtual kompleks yang spesifik, dimana fungsi keanggotaan non linear dapat mengendalikan robot untuk menghindari halangan pada lingkungan virtual spesifik yang kompleks dengan lebih smooth dan lebih baik

A Simple Goal Seeking Navigation Method for a Mobile Robot using Human Sense, Fuzzy Logic and Reinforcement Learning

Abstract — This paper proposes a new fuzzy logic-based navigation method for a mobile robot moving in an unknown environment. This method allows the robot obstacles avoidance and goal seeking without being stuck in local minima. A simple Fuzzy controller is constructed based on the human sense and a fuzzy reinforcement learning algorithm is used to fine tune the fuzzy rule base parameters. The advantages of the proposed method are its simplicity, its easy implementation for industrial applications, and the robot joins its objective despite the environment complexity. Some simulation results of the proposed method and a comparison with previous works are provided

Control of Real Mobile Robot Using Artificial Intelligence Technique

An eventual objective of mobile robotics research is to bestow the robot with high cerebral skill, of which navigation in an unfamiliar environment can be succeeded by using on‐line sensory information, which is essentially starved of humanoid intermediation. This research emphases on mechanical design of real mobile robot, its kinematic & dynamic model analysis and selection of AI technique based on perception, cognition, sensor fusion, path scheduling and analysis, which has to be implemented in robot for achieving integration of different preliminary robotic behaviors (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimization problem and thus can be analyzed and solved using AI techniques. The optimization of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A set of linguistic fuzzy rules are developed to implement expert knowledge under various situations. Both of Mamdani and Takagi-Sugeno fuzzy model are employed in control algorithm for experimental purpose. Neural network has also been used to enhance and optimize the outcome of controller, e.g. by introducing a learning ability. The cohesive framework combining both fuzzy inference system and neural network enabled mobile robot to generate reasonable trajectories towards the target. An authenticity checking has been done by performing simulation as well as experimental results which showed that the mobile robot is capable of avoiding stationary obstacles, escaping traps, and reaching the goal efficiently

Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques

The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques