Fuzzy Logic Controller sebagai Penentu Gerak Mobile Robot Pembasmi Hama

A mobile robot is one of the solutions to overcome crop failure caused by chili pests. The mobile robot discussed in this paper is used to spray pesticide liquid into chili plant stems to prevent pests attack on the plants. This paper discusses the design of pesticide spraying robot motion with the application of Fuzzy Logic Controller. This robot employment is expected to reduce farmers' workload and to help to produce a good harvest.  Robot motions are divided into two conditions, which can be controlled by remote control as a controller (manual) and by means of a sensor (automatic). Mobile robot movements have a significant impact on navigation and the design of the driving system. Robot speed is controller by adjusting Pulse Width Modulation of DC motors attached to the robots' wheel, which set to be  90 for slow and 220  for high speed. The Fuzzy Logic Controller in this mobile robot functions as an autonomous decision-making driver to detect obstacles in front of the mobile robot and the targeted stems

Implementation of Takagi Sugeno method as fuzzy logic control with multiple sensors on fire fighter robot prototype navigation

Autonomous mobile robot will become an interest by many researchers. Due to needs of an effective navigational system for the robot, many approach was purposed to control the reliability of the system in order to achieve an optimum control system respond. Nowadays, AI was used as one of the approached to enhance the decision making by the robot itself without depending on the user. In this development, multiple sensors were implemented to increase the detection accuracy of the prototype during navigate on constantly changing environment. By using multiple sensors (ultrasonic sensor) it means that, it will have more input information so, in order to manage all of this data information, intelligent control strategy was required to achieve an optimum output in term of efficiency and accuracy. On this development, previous prototype was upgraded into more intelligent system. Fuzzy Logic was introduced to this development by using a Takagi Sugeno inference method. In this project Fuzzy control was designed with aid of computing software (MATLAB) and by using a Arduino MEGA controller Fuzzy logic control was apply to realize the real-time simulation for the robot navigation. The fire detection unit by using IR sensor module was purposed to make sure it can scan the IR wave generated by the flame. Differential steering method was selected for semi-autonomous robot driving system and it was powered by 2 DC motor for the robot navigation. All the function of the robot is tested in order to evaluate the capability of the system on the robot by referred to the project scope. On this development, the effectiveness of the system was vary with the number of Fuzzy Rule. The effectiveness of the Fuzzy control was observed by the time taken for the prototype completed the route and the time respond for the prototype to avoid obstacle during the navigation inside the test rig

Simulation and Design of an Intelligent Mobile Robot for Fire Fighting

The application of traditional frangible glass panel and automatic system such as smoke detector or heat detector requires a human response to realise the existence of fire and to perceive and determine its severity. However, fire-fighting system such as sprinkler causes the damage of the property and also injury and panic due to the water sprayed out when the system trigged. Thus to avoid such incident, a mobile robot with fire detection capability and fire fighting system to perform fire-fighting purpose is a new technology to reduce subsequent damage and secure life before the fire engine to attend. A navigation system base on fuzzy logic controller (FLC) is developed for the mobile robot in an ambiguous situation for fire fighting purpose. A method of Path Recognition Algorithms (PRA) providing robots the autonomous ability to judge purpose of action likes human base on the input sensors from the environment. Multiple fuzzy behaviours by fuzzy logic method have been developed to allow the robot over come some of the possible obstacles and resistances for the robot to navigate in unknown environment. Behaviours have been integrated with arbitration strategy to determine the appropriate behaviours by priority method with preset data. An ultrasonic sensor and an infrared thermal sensor were mounted on a 360 degree rotated stepper motor to scan distance between the robot and its immediate obstacles and fire source around its environment. A fuzzy base computer animation in virtual reality is developed to simulate a simple in-door environment for fire fighting purpose and a systematic implementation of real-time simulation of the mobile robot is presented. However traveling in such region to the target location, there exist some unknown obstacles for the mobile robot especially in real-world environment with unknown map and unpredictable obstacle location, thus the control algorithms must be able to promptly react upon the unpredictable. The simulation result of this study indicated that application of FLC in mobile robot could be a suitable system for fire detection and fire fighting task in an unknown environment. More comprehensive study in behaviour coordination will be the major focus to ensure smoother robot navigation and more effective fire detection capability. Keywords: (Fuzzy logic control, mobile robot, part recognition algorithms

Navigation of Mobile Robot using Fuzzy Logic

In this paper research has been carried out to develop a navigation technique for an autonomous robot to work in a real world environment, which should be capable of identifying and avoiding obstacles, specifically in a very busy a demanding environment. In this paper better technique is develop in navigating mobile robot in above mention environment. The action and reaction of the robot is addressed by fuzzy logic control system. The input fuzzy members are turn angle between the robot head and the target, distance of the obstacles present all around the robot (left, right, front, back).The above mention input members are senses by series of infrared sensors. The presented FLC for navigation of robot has been applied in all complex and adverse environment. The results are hold good for all the above mention conditions

Fuzzy logic control for Semi-Autonomous navigation robot using integrated remote control

Along with the times, technology in the field of mobile robots continues to progress very rapidly. In the development of autonomous robots, navigation is one part that has an important role. Therefore, mobile robots must be able to adapt to their environment. So we need a control method that can help the robot in the process of adjusting the dynamics of the surrounding environment. In this study, the semi-automatic navigation robot adopts artificial intelligence Fuzzy logic as an output processor that will be generated by the robot. Fuzzy logic on this robot is used to control the speed of the motor based on the distance of the obstacle that is read by the sensor and the input provided by the remote control. In this research, one ultrasonic sensor HC-SR04 is used which is mounted on the front of the robot, and remote control to give commands to the robot and Arduino MEGA 2560 as the microcontroller. To make the robot's movement more stable when avoiding obstacles, a fuzzy logic algorithm is applied to control the right and left motor PWM. Fuzzy robot system testing is carried out with a robot scenario detecting obstacles at a distance of 4cm and the remote providing an input value of 1870Hz. The results shown on the Arduino IDE application serial monitor are 62.5 PWM for the right motor and 103.53 PWM for the left motor, while the simulation results in the Matlab application show that the right motor PWM is 62.5 PWM and the left motor is 104 PWM. By comparing the output of the semi-automatic navigation robot based on Fuzzy with the output of the simulation results, it is found that fuzzy logic has been successfully implemented on the robot with a success rate of 100% for the right motor and 99.995% for the left motor

Design and analysis of Intelligent Navigational controller for Mobile Robot

Since last several years requirement graph for autonomous mobile robots according to its virtual application has always been an upward one. Smother and faster mobile robots navigation with multiple function are the necessity of the day. This research is based on navigation system as well as kinematics model analysis for autonomous mobile robot in known environments. To execute and attain introductory robotic behaviour inside environments(e.g. obstacle avoidance, wall or edge following and target seeking) robot uses method of perception, sensor integration and fusion. With the help of these sensors robot creates its collision free path and analyse an environmental map time to time. Mobile robot navigation in an unfamiliar environment can be successfully studied here using online sensor fusion and integration. Various AI algorithm are used to describe overall procedure of mobilerobot navigation and its path planning problem. To design suitable controller that create collision free path are achieved by the combined study of kinematics analysis of motion as well as an artificial intelligent technique. In fuzzy logic approach, a set of linguistic fuzzy rules are generated for navigation of mobile robot. An expert controller has been developed for the navigation in various condition of environment using these fuzzy rules. Further, type-2 fuzzy is employed to simplify and clarify the developed control algorithm more accurately due to fuzzy logic limitations. In addition, recurrent neural network (RNN) methodology has been analysed for robot navigation. Which helps the model at the time of learning stage. The robustness of controller has been checked on Webots simulation platform. Simulation results and performance of the controller using Webots platform show that, the mobile robot is capable for avoiding obstacles and reaching the termination point in efficient manner

Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

A layered fuzzy logic controller for nonholonomic car-like robot

A system for real time navigation of a nonholonomic car-like robot in a dynamic environment consists of two layers is described: a Sugeno-type fuzzy motion planner; and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including right and left views to identify the next step to the goal. A Sugeno-type fuzzy motion planner of four inputs one output is introduced to give a clear direction to the robot controller. The second stage is a modified proportional navigation based fuzzy controller based on the proportional navigation guidance law and able to optimize the robot's behavior in real time, i.e. to avoid stationary and moving obstacles in its local environment obeying kinematics constraints. The system has an intelligent combination of two behaviors to cope with obstacle avoidance as well as approaching a target using a proportional navigation path. The system was simulated and tested on different environments with various obstacle distributions. The simulation reveals that the system gives good results for various simple environments