An hybridization of global-local methods for autonomous mobile robot navigation in partially-known environments

This paper deals with the navigation problem of an autonomous non-holonomic mobile robot in partially-known environment. In this proposed method, the entire process of navigation is divided into two phases: an off-line phase on which a distance-optimal reference trajectory enables the mobile robot to move from an initial position to a desired target which is planned using the B-spline method and the Dijkstra algorithm. In the online phase of the navigation process, the mobile robot follows the planned trajectory using a sliding mode controller with the ability of avoiding unexpected obstacles by the use of fuzzy logic controller. Also, the fuzzy logic and fuzzy wall-following controllers are used to accomplish the reactive navigation mission (path tracking and obstacle avoidance) for a comparative purpose. Simulation results prove that the proposed path planning method (B-spline) is simple and effective. Also, they attest that the sliding mode controller track more precisely the reference trajectory than the fuzzy logic controller (in terms of time elapsed to reach the target and stability of two wheels velocity) and this last gives best results than the wall-following controller in the avoidance of unexpected obstacles. Thus, the effectiveness of our proposed approach (B-spline method combined with sliding mode and fuzzy logic controllers) is proved compared to other techniques

Path Navigation For Robot Using Matlab

Path navigation using fuzzy logic controller and trajectory prediction table is to drive a robot in the dynamic environment to a target position,without collision. This path navigation method consists of static navigation method and dynamic path planning. The static navigation used to avoid the static obstacles by using fuzzy logic controller, which contains four sensor input and two output variables. If the robot detects moving obstacles, the robot can recognize the velocity and moving direction of each obstacle and generate the Trajectory Prediction Table to predict the obstacles’ future trajectory. If the trajectory prediction table which reveals that the robot will collide with an obstacle, the dynamic path planning will find a new collision free path to avoid the obstacle by waiting strategy or detouring strategy. . A lot of research work has been carried out in order to solve this problem. In order to navigate successfully in an unknown or partially known environment, the mobile robots should be able to extract the necessary surrounding information from the environment using sensor input, use their built-in knowledge for perception and to take the action required to plan a feasible path for collision free motion and to reach the goal

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

Design Of Navigation Algorithm For F_Bot For Patrolling Using Fuzzy Logic

The main objective of this project is to build a mobile robot that would be able to navigate from one position to the other assigned position. It is also able to determine the shortest path towards the goal position without hitting any obstacle along the path. This mobile robot uses the Fuzzy Logic controller to navigate the robot in varying environments to a specified position. The implementation has been carried out using inexpensive components and tools. As the mobile robot is using the fuzzy logic controller to control its movement, it is named as F_Bot The F_Bot has two front wheels navigated by two servo motors 5V and a free castor wheel is placed at front. The robot will be moving towards the specified goal position determined by the user by using the dead reckoning method. The data acquisition is done by the PIC microcontroller from various sensors including ultrasonic range detector sensor, infrared sensor and encoder. The data will be sent to the computer using serial data transmission method. The data will be processed by the computer using Matlab and Fuzzy Logic to get the correct angle and the acceleration for the robot. This data will be sent back to the PIC microcontroller to control the motors for navigation purposes. The servo motors are easily controlled by feed-in pulses directly from the PIC microcontroller. The PIC microcontroller 16F877 can perform a large number of applications especially for control and sensing applications. In addition, MATLAB is a standard and cost- effective tool within the engineering community for scientific applications. The Fuzzy Logic is used here to predict the direction of movement and the speed for the robot. Since the input data for predicting the direction and speed is too large, the fuzzy logic is used for fast prediction

Fuzzy logic controlled miniature LEGO robot for undergraduate training system

Fuzzy logic enables designers to control complex systems more effectively than traditional approaches as it provides a simple way to arrive at a definite conclusion upon ambiguous, imprecise or noisy information. In this paper, we describe the development of two miniature LEGO robots, which are the line following and the light searching mobile robots to provide a better understanding of fuzzy logic control theory and real life application for an undergraduate training system. This study is divided into two parts. In the first part, an object sorter robot is built to perform pick and place task to load different colour objects on a fuzzy logic controlled line following robot which then carries the preloaded objects to a goal by following a white line. In the second part, an intelligent fuzzy logic controlled light searching robot with the capability to navigate in a maze is developed. All of the robots are constructed by using the LEGO Mindstorms kit. Interactive C programming language is used to program fuzzy logic robots. Experimental results show that the robots has successfully track the predefined path and navigate towards light source under the influence of the fuzzy logic controller; and therefore can be used as a training system in undergraduate fuzzy logic class

PATH FOLLOWING BEHAVIOR FOR AN AUTONOMOUS MOBILE ROBOT

In order to achieve tasks by the mobile robots, these robotic systems must have been intelligent and should decide their own action. To guarantee the autonomy and the intelligence for the path following behavior, it is necessary to use the techniques of artificial intelligence like the neural networks and the fuzzy logic. This paper presents an approach for the path following task by an autonomous mobile robot using neural networks and fuzzy logic controllers. The first controller is a Takagi-Sugeno fuzzy model and the second is a multi-layer neural network. The proposed controllers are used for pursing a moving target. The results are compared and discussed

Intelligent Fuzzy Logic based controller scheme for a mobile robot navigation

The unhindered navigation of mobile robots in an unstructured and dynamic environment is constrained by uncertainty, unreliability of input information, and unpredictability issues that plague the sensors and the robot controller. One of the long standing challenges in modern day mobile robotics is instilling the ability and intelligence in robots to autonomously navigate their path, avoiding structured and unstructured obstacles in real-time. An effective way of structuring the navigation path is designing the robot controller by implementing behavioral based approaches. In this project, research work has been carried out on the different fuzzy techniques which can be implemented for the navigation of a wheeled mobile robot, especially in a crowded and unpredictably dynamic environment and in the midst of static as well as dynamic obstacles. In this project, individual robot behaviors and their action coordination are addressed using fuzzy logic. It uses sets of linguistic fuzzy rules to implement expert knowledge in various situations. Later, it has been shown that the fuzzy model of the robot controller far outweighs the traditional algorithm based approach towards design of a robot control system. The proposed fuzzy scheme consists of inputs from an array of sensors located at the front, sides and rear of the mobile robot, which provide information about distances between obstacles to the front, left, right and back of the robot and the fuzzy rule base is run over these inputs to actuate the motion of the left and right wheels of the robot as per the situtation encountered

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

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