Mohammed Faisal

Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment

Mobile robot navigation has remained an open problem over the last two decades. Mobile robots are required to navigate in unknown and dynamic environments, and in recent years the use of mobile robots in material handling has considerably increased. Usually workers push carts around warehouses and manually handle orders which is not very cost-effective. To this end, a potential method to control a swarm of mobile robots in a warehouse with static and dynamic obstacles is to use the wireless control approach. Further, to be able to control different types of mobile robots in the warehouse, the fuzzy logic control approach has been chosen. Therefore, in this paper, an on-line navigation technique for a wheeled mobile robot (WMR) in an unknown dynamic environment using fuzzy logic techniques is investigated. In this paper, we aim to use the robot in application in a warehouse. Experimental results show the effectiveness of the proposed algorithm.

A Hierarchical Fuzzy Control Design for Indoor Mobile Robot

This paper presents a motion control for an autonomous robot navigation using fuzzy logic motion control and stereo vision based path-planning module. This requires the capability to maneuver in a complex unknown environment. The mobile robot uses intuitive fuzzy rules and is expected to reach a specific target or follow a prespecified trajectory while moving among unforeseen obstacles. The robots mission depends on the choice of the task. In this paper, behavioral-based control architecture is adopted, and each local navigational task is analyzed in terms of primitive behaviors. Our approach is systematic and original in the sense that some of the fuzzy rules are not triggered in face of critical situations for which the stereo vision camera can intervene to unblock the mobile robot.

Robot localization using extended kalman filter with infrared sensor

In order to use mobile robot for any application, mobile robot should have an accurate pose information. Most of the localization systems are based on the odometry sensors or the map of the environment, Therefore, localization is a major requirement for a mobile robot. The navigating operation in mobile robot usually uses the odometry sensors to estimate its position. These odometry sensors reckoning the number of revolutions that the wheels make while driving and turning. This reading of the wheel is used to estimating the displacement over the ground to give a make of the location of the robot. This way of localization has many problem, such as wheel slippage, surface roughness, and mechanical tolerances. These papers, propose a mobile robot localization system based on the extended kalman filter and infrared sensor to overcome the problems of localization in mobile robot. The experimental result of this paper illustrates the robust and the accuracy of the proposed system.

AntStar: enhancing optimization problems by integrating an Ant System and A * algorithm

Recently, nature-inspired techniques have become valuable tomany intelligent systems in different fields of technology and science. Among these techniques, Ant Systems (AS) have become a valuable technique for intelligent systems in different fields. AS is a computational system inspired by the foraging behavior of ants and intended to solve practical optimization problems. In this paper, we introduce the AntStar algorithm, which is swarm intelligence based. AntStar enhances the optimization and performance of an AS by integrating the AS and A* algorithm. Applying the AntStar algorithm to the single-source shortest-path problem has been done to ensure the efficiency of the proposed AntStar algorithm. The experimental result of the proposed algorithm illustrated the robustness and accuracy of the AntStar algorithm.

SDTP: Secure data transmission protocol in Ad Hoc Networks based on link-disjoint multipath routing

Mobile Ad Hoc Networks (MANETs) have taken a rapid growth of research. The infrastructure-less and the dynamic nature of these networks demand new set of networking strategies to be implemented in order to provide efficient end-to-end communication. Due to the nature of MANETs, they are highly vulnerable to security attacks. One of the main issues of MANETs is the security. In this paper, we propose a new protocol to ensure secure data transmission in ad hoc networks (Secure Data Transmission Protocol SDTP) based on link-disjoint multipath routing, and Key Management Models. The proposed protocol ensures the confidentiality, integrity, authentication, and availability of data transmission in ad hoc networks. Ad hoc network characteristics should be taken into consideration to be able to design efficient solutions. This paper takes advantage of multiple paths between nodes in MANETs to ensure the security.

Visual Tracking in Unknown Environments Using Fuzzy Logic and Dead Reckoning

This paper addresses the problem of making a non-holonomic wheeled mobile robot (WMR) move to a target object using computer vision and obstacle-avoidance techniques. If a priori information about the obstacles is available, pre-planning the desired path can be a good candidate method. However, in so many cases, obstacles are dynamic. Therefore, our first challenge is to make the WMR move to a desired target while autonomously avoiding any obstacle along its path. The second challenge deals with visual-tracking loss; that is, when the target is lost from the camera scope, the robot should use Dead Reckoning (DR) to get back on its path towards the target. The Visual Tracking (VT) algorithm then takes the relay to reach the final destination, compensating for any errors due to DR by calculating the distance to the target when it is within the scope of the camera. The proposed system also uses two fuzzy-logic controllers; the first controller avoids objects while the second manages the path to the target. Different complex scenarios have been implemented, showing the validity of our multi-controller model.

An autonomous stereovision-based navigation system (ASNS) for mobile robots

Recently, stereovision has appeared in robotics as a source of information for real-time mapping and path planning. In this paper, an intelligent motion system for mobile robots is designed and implemented using stereovision. The proposed system uses stereovision as a primary method for sensing the environment, and the system is able to navigate intelligently in an indoor environment with varying degrees of obstacle complexity. It creates noiseless and high-confidence 3D point clouds and uses these point clouds as an input for the mapping and path-planning modules. The proposed system was built by developing, enhancing, and integrating various techniques, modules and algorithms. The Stereovision-based Path-planning module is the integration of three main enhanced techniques: (1) the multi-baseline multi-view stereovision filter (MMSVF), (2) accurate floor detection and segmentation (AFDS), and (3) the intelligent gazing module (IGM). This Stereovision-based Path planning (MMSVF, IGM, and AFDS) was integrated with the Fuzzy Logic Motion Controller (FLMC). All techniques, modules and algorithms are implemented using a multi-threaded and client–server-based architecture. To prove the viability and robustness of our proposed system, we have integrated all components of the system into a fully functional mobile robot navigation system. We compared the performance of the main modules with that of similar modules in the literatures, and showed that our modules had better performance. Testing the whole system is more important than just testing each module individually. To the best of our knowledge, the literatures lack such testing. Hence, in this paper we present the performance of our complete integrated system in different environments using different parameters and different architectures.

Enhancement of mobile robot localization using extended Kalman filter

In this article, we introduce a localization system to reduce the accumulation of errors existing in the dead-reckoning method of mobile robot localization. Dead-reckoning depends on the information that comes from the encoders. Many factors, such as wheel slippage, surface roughness, and mechanical tolerances, affect the accuracy of dead-reckoning. Therefore, an accumulation of errors exists in the dead-reckoning method. In this article, we propose a new localization system to enhance the localization operation of the mobile robots. The proposed localization system uses the extended Kalman filter combined with infrared sensors in order to solve the problems of dead-reckoning. The proposed system executes the extended Kalman filter cycle, using the walls in the working environment as references (landmarks), to correct errors in the robot’s position (positional uncertainty). The accuracy and robustness of the proposed method are evaluated in the experiment results’ section.

Multi-sensors multi-baseline mapping system for mobile robot using stereovision camera and laser-range device

Countless applications today are using mobile robots, including autonomous navigation, security patrolling, housework, search-and-rescue operations, material handling, manufacturing, and automated transportation systems. Regardless of the application, a mobile robot must use a robust autonomous navigation system. Autonomous navigation remains one of the primary challenges in the mobile-robot industry; many control algorithms and techniques have been recently developed that aim to overcome this challenge. Among autonomous navigation methods, vision-based systems have been growing in recent years due to rapid gains in computational power and the reliability of visual sensors. The primary focus of research into vision-based navigation is to allow a mobile robot to navigate in an unstructured environment without collision. In recent years, several researchers have looked at methods for setting up autonomous mobile robots for navigational tasks. Among these methods, stereovision-based navigation is a promising approach for reliable and efficient navigation. In this article, we create and develop a novel mapping system for a robust autonomous navigation system. The main contribution of this article is the fuse of the multi-baseline stereovision (narrow and wide baselines) and laser-range reading data to enhance the accuracy of the point cloud, to reduce the ambiguity of correspondence matching, and to extend the field of view of the proposed mapping system to 180°. Another contribution is the pruning the region of interest of the three-dimensional point clouds to reduce the computational burden involved in the stereo process. Therefore, we called the proposed system multi-sensors multi-baseline mapping system. The experimental results illustrate the robustness and accuracy of the proposed system.

Smart mobile robot for security of low visibility environment

The field of robotics has apparently appeared to replace jobs that humans usually dislike to do, jobs that have low tolerance for faults and risks or jobs for which human resource is not available. For these reasons, many types of robot have appeared, such as man-like robots, car-like robots and mobile robots. Mobile robots have become a target for many applications, such as security patrolling, manufacturing, materials handling in the warehouse, and many others applications. In the last decade, mobile robots have become an important topic in the security field. Several methods have been introduced to deal with mobile robot and security. Nevertheless, most of these methods are not smart enough to work in low visibility environments. In this paper, we are going to introduce a novel smart security system, which uses a mobile robot as a security patrol in dark environment. The proposed system is a non-vision based system and designed to deal with the dark environments using artificial intelligence methods (Neural Network NN and Fuzzy Logic FL). The proposed system includes a motion control model, which is responsible for the navigation operation in the monitoring area. The NN and FL techniques are used to smartly determine the abnormal situation in the monitoring area. Many levels of alarms are used to tackle the deferent level of dangers.