Navigational strategies of mobile robots: a review

Navigation system for an autonomous robot is an area that is undergoing constant development. This paper describes anautonomous robot that is capable of navigating in a real time environment. This can be achieved by obtaining the information about robot’s environment by using sensors and process it. For implementation of obstacle avoidance, Fuzzy Logic approach is used and is implemented using Arduino-Uno board on mobile robot platform with three sets of ultrasonic sensors mounted on it. The Fuzzy Logic approach allows us to use the ultrasonic or infrared sensors that allow fast and cost effective distance measurements with varying uncertainty.

International Journal of Electrical and Computer …, 2011

This paper presents sensor-based intelligent mobile robot navigation in unknown environments. The paper deals with fuzzy control of autonomous mobile robot motion in an unknown environment with obstacles and gives a wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Sun SPOT technology. Simulation results show the effectiveness and the validity of the obstacle avoidance behavior in an unknown environment and velocity control of a wheeled mobile robot motion of the proposed fuzzy control strategy. The proposed remote method has been implemented on the autonomous mobile robot Khepera that is equipped with sensors and the free range Spot from the Sun Spot technology. Finally, the effectiveness and the efficiency of the proposed sensor-based remote control strategy are demonstrated by experimental studies and good experimental results of the obstacle avoidance behavior in unknown environments.

Recent Advances in Mobile Robotics, 2011

The use of fuzzy sets to represent the approximate positions and possibly shapes of objects in the environment. • The design of simple fuzzy behaviors (avoiding obstacles, goal reaching, wall following…etc.). • The blending of the different fuzzy behaviors. 2. Behavior based navigation One of the long standing challenging aspect in mobile robotics is the ability to navigate autonomously, avoiding modeled and unmodeled obstacles especially in crowded and unpredictably changing environment. A successful way of structuring the navigation task in order to deal with the problem is within behavior based navigation approaches (

2012

A new paradigm of intelligent navigation system for mobile robot has been enriched with some common features like: criteria for optimal performance and ways to optimize design, structure and control of robot. With the growing need for the deployment of intelligent and highly autonomous systems, it would be beneficial to flawlessly combine robust learning capabilities of artificial neural networks with a high level of knowledge interpretability provided by fuzzy-logic. Fuzzy-neural network is able to build comprehensive knowledge bases considering sensor-rich system with real time constraints by adaptive learning, rule extraction and insertion, and neural/fuzzy reasoning. This technique is simulated and also compared with other simulation studies by previous researcher . The training for back propagation algorithm and its navigational performances analysis has been done in real experimental setup. As experimental result matches well with the simulation result, the realism of method i...

The use of robots is one of the promising areas for development in various industries, human activities. Mobile robots are of particular importance. These robots are able to replace humans in difficult and dangerous situations. Mobile robots are able to perform any tasks that have different levels of difficulty. An important element of mobile robots is the navigation system and the management of such a system. The navigation control system of a mobile robot determines the possibilities of using such a robot. This necessitated the importance of considering the features of the construction and control of the navigation system of a mobile robot. The paper highlights the key features of this consideration.

Applied Soft Computing, 2009

Cogent Engineering

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Robotica, 1997

Most of the motion controls of the mobile robots are based on the classical scheme planning-navigation-piloting. The navigation function the main part of which consists in obstacle avoidance, has to react with the shortest response time. The real time constraint hardly limits the complexity of sensor data processing. The described navigator is built around fuzzy logic controllers. Besides the well-known possibility of taking into account human know-how, the approach provides several contributions : a low sensitivity to erroneous or inaccurate measures and, if the inputs of the controllers are normalised, a well to do portability on various platform. To show these advantages, the same fuzzy navigator has been implemented on two mobile robots. Their mechanical structures are close, except for the size and the sensing system.

Proceedings of the 9th Wseas International Conference on Fuzzy Systems, 2008

The paper presents fuzzy navigation system for a mobile robot. This navigation is based on fuzzy inference machine that performs path planning and obstacle avoidance in arbitrary complex environments. The system is built as two-level hierarchical system. On the higher level there is a controlling PC that is connected with robot control unit through duplex, wireless communication channel. A sensor system comprising four subsystems: tactile, ultrasonic, odometer, and visual is cared for collection of environment information. The inference machine consists of two mutual connected fuzzy rule bases responsible for path planning and obstacle avoidance. The fuzzy navigation strategy is obtained as a combined compromise decision between them.

International Journal of Computer Applications, 2018

Autonomous mobile robots' navigation has become a very popular and interesting topic of computer science and robotics in the last decade. Many algorithms have been developed for robot motion control in an unknown (indoor/outdoor) and in various environments (static/dynamic). Fuzzy logic control techniques are an important algorithm developed for robot navigation problems. The aim of this research is to design and develop a fuzzy logic controller that enables the mobile robot to navigate to a target in an unknown environment, using WEBOTS commercial mobile robot simulation and MATLAB software. The algorithm is divided into two stages; In the first stage, the mobile robot was made to go to the goal, and in the second stage, obstacle avoidance was realized. Robot position information (x, y, Ø) was used to move the robot to the target and six sensors data were used during the obstacle avoidance phase. The used mobile robot (E_PUCK) is equipped with 12 IR sensors to measure the distance to the obstacles. The fuzzy control system is composed of six inputs grouped in doubles which are left, front and right distance sensors two outputs which are the mobile robot's left and right wheel speeds. To check the simulation result for proposed methodology, WEBOTS simulator and MATLAB software were used. To modeling the environment in different complexity and design, this simulator was used. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for autonomous mobile robots and the objective of this research has been successfully achieved. This research also indicated that WEBOT and MATLAB are suitable tools that could be used to develop and simulate mobile robot navigation system.

1999

One of the problems associated with traditional behaviour based navigation systems for mobile robots is that of command arbitration. Having multiple behaviours which are all running concurrently leads to situations where several command outputs may be produced simultaneously. The decision about which output should be used to drive the robot is left to a separate command arbitrator. Using this method, however, does not always result in smooth motion control of the robot and may even cause the robot to move in a completely unintended direction. This paper describes the design of a fuzzy logic based navigation system for a mobile robot. The advantage of using fuzzy logic for navigation is that it allows for the easy combination of various behaviours' outputs through a command fusion process. The navigation system in this case consists of two behaviours -an obstacle avoidance behaviour and a goal seeking behaviour. The inputs to the fuzzy controller are the desired direction of motion and the readings from the sensor array. The outputs from each behaviour's rule base are integrated using the command fusion process and made crisp using a modified defuzzification technique. The end result is very smooth motion control of the robot.

Iranian Journal of Medical Informatics

Introduction: In recent years, topics related to robotics have become one of the areas of research and development. In the meantime, smart robots are very popular, but the control and navigation of these devices are very difficult, and the lack of handling and staggering obstacles and avoidance of them, due to safe and secure routing, outweigh the basic needs of these systems. Material and Methods: In this research, for the purpose of solving the intelligent navigation problem, a moving robot in a dynamic unknown environment (conditions at any moment in the range of moving and obstacles in motion) and the choice of optimal path, the methods of genetic algorithm and fuzzy logic are used comparatively. Results: By using genetic algorithm and fuzzy logic methods, the robot can move in the dynamic and unknown environment to the optimal path to the target. Conclusion: Information about the environment is also necessary to avoid obstacles, optimal path design and environment exploration, and to establish a clever relationship between perception and practice that requires the use of appropriate algorithms such as the genetic algorithm and fuzzy logic (fuzzy controller) are also needed to manage the control and navigation. Article History

International Journal of Computer Applications, 2012

The present article is devoted to develop an algorithm for obstacle avoidance of an autonomous mobile robot based on fuzzy logic/ The method of navigation proposed provides a way of blending the intelligence and optimality of global methods with the reactive dynamic behavior of local ones. This is achieved by using hybrid navigation system composed of two modules, one of which uses the apriori information and determines roughly the optimal route towards the goal, whereas the other carries out effective navigation decisions using the potential function based local approach. The fuzzy rules are constructed from intuitive and subjective human ways of collision avoidance. The results of the present study are compares favorably with those of well-established algorithms.

Computing Research Repository, 2010

In recent years, the use of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, a theoretical model of a fuzzy based controller for an autonomous mobile robot is developed. The paper begins with the mathematical model of the robot that involves the kinematic model. Then, the fuzzy logic controller is developed and discussed in detail. The proposed method is successfully tested in simulations, and it compares the effectiveness of three different set of membership of functions. It is shown that fuzzy logic controller with input membership of three provides better performance compared with five and seven membership functions.

Robotics and Computer-integrated Manufacturing, 2009

A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose. Training to the SC-based approaches is given off-line and the performance of the optimal motion planner has been tested on a real robot. Results of the SC-based motion planners have been compared between themselves and with those of the conventional PFM. Both the SC-based approaches are found to perform better than the PFM in terms of traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is seen to be comparable with that of neural network-based motion planner. Comparisons among all these three motion planning schemes have been made in terms of robustness, adaptability, goal reaching capability and repeatability. Both the SC-based approaches are found to be more adaptive and robust compared to the PFM. It may be due to the fact that there is no in-built learning module in the PFM and consequently, it is unable to plan the velocity of the robot properly.