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Today, with the continuous advancement of technological developments, autonomous and semi-autonomous vehicles facilitate our operations in many areas such as personal and commercial transportation, agriculture, construction and mining, research and exploration. Among these vehicles are autonomous mobile robots (AMRs), which are frequently preferred in factories. One of the reasons why AMRs are preferred is that there is insufficient manpower in the areas where they are used. Secondly, these robots can eliminate risky situations that can threaten the health of workers. In addition, AMRs have the potential to save manpower. In this study, we aim to provide data security for AMRs using wireless communication technologies by using a hybrid encryption method. The security performance of asymmetric encryption and the fast communication advantages of symmetric encryption make it necessary to use both encryption methods together for AMRs. In the hybrid encryption approach, the asymmetric encryption method ECC (Elliptic Curve Cryptography) and the symmetric encryption method AES (Advanced Encryption Standard) are used together. ECDSA (Elliptic Curve Digital Signature Algorithm), the digital signature method based on the ECC encryption method, was used as a reference. In the AES encryption method, GCM (Galois/Counter Mode) is preferred since verification is performed in addition to encryption. In this study, AMRs, which communicate with each other or with the controller system and act as data senders, were encrypted using ECDSA-AES/GCM hybrid encryption method using Python programming language and their performance was evaluated. In this way, the protection of the confidentiality and integrity of the transmitted data in case of a possible data leakage during wireless communication will be examined. As a result, this proposed method aims to provide an important step in terms of data security for autonomous mobile robots using wireless communication. [ABSTRACT FROM AUTHOR]

In this paper, path following control of an unmanned ground vehicle is presented based on the feedback of position and orientation errors. The measurement unit of this autonomous vehicle prototyped for such tasks contains a real-time kinematic global positioning system (RTK-GPS), an inertial measurement unit (IMU) and an absolute encoder to accurately define the position and orientation of the car. A model predictive control was proposed for the path following of the mobile robot based on the successive linearized and discretized kinematic model. This optimal control method performs on the lowest position and orientation errors with respect to a non-holonomic virtual vehicle that is considered to move flawlessly on a given reference path and the smoother steering angle. The paths followed here are defined by rationally based splines or known geometric curves created with control points from a digital mapping program. This paper includes both simulation and real-time experimental studies. The outcomes were examined in terms of the design performance and control strategy of the vehicle. Despite the physical constraints on the vehicle prototype, it has been observed that position and orientation errors occur within satisfactory limits. In particular, the fact that the steering angle is not subjected to excessive oscillations indicates that the control method has a good performance. [ABSTRACT FROM AUTHOR]

In this study, the design and analysis of Autonomous Mobile Robot (AMR) is investigated. These robots perform the function of loading the cargo in factories or industrial facilities autonomously without any need for an operator and driver. These features of AMR’s have provided them with a wide range of uses. An original AMR design was performed for this study. Deformation and stress analysis of AMR has been made at three loads including 1.000, 1.500, and 2.000 kg. While the design was performed, it was considered that the deformation and stress occurring in the carrier chassis of AMR were independent of the load. This feature of the design has been proven with the help of the simulation. The strength of the chassis was found to be approximately the same for all three loads in the analysis results. Therefore, AMR has been obtained that is independent of the dimensions and structure of the chassis. It can carry the total capacity of the carrier wheels. [ABSTRACT FROM AUTHOR]

Nowadays, the problem of mobile robots to reach target points with optimum cost has become an important field of study. Although the optimum cost varies in studies, in general, time spent, distance, energy spent, or different combinations in which these are evaluated together. Especially in complex environments with a large number of obstacles, improvements are generally made on the algorithm side in order to solve the problems in acceptable time. This study focuses on improvement on the problem side. In this context, a hybrid model is proposed in which a metaheuristic algorithm and a clustering algorithm are used together in order to reduce the complexity of the environment by clustering the obstacles in static and two-dimensional environments and thus increase the running speed of optimization algorithms. For the proposed model, a detailed analysis has been performed first. In this analysis, particle swarm optimization (PSO) as metaheuristic algorithm and k-means clustering algorithm are used as clustering algorithm. As a result of this analysis, which was tested with various clustering rates, as the clustering rate increased, small losses were obtained in terms of shortest distance path, but the running speed of the algorithm increased at a level that could compensate these losses. In addition, effectiveness of the proposed model was evaluated comparatively on different metaheuristic and clustering algorithms. The results show that speed of path planning algorithms can be increased by the proposed model for two-dimensional environments with a large number of randomly located obstacles. [ABSTRACT FROM AUTHOR]

Periodic maintenance and malfunction/damage detection are usually carried out manually, by maintenance personnel. Although there are some situations where simple observations are sufficient for fault detections, measuring instruments are generally needed. On the other hand, direct intervention of maintenance personnel is also required in many cases to eliminate malfunctions. Because of the fact that it is not yet possible in today’s technology to transfer all the activities performed by the maintenance personnel to the robots, the robots used in maintenance, fault detection and repair are seen as auxiliary equipment for maintenance personnel. As there is no one robot useable for all sorts of maintenance needs, many applications call for task-based robots. In this study, applications and benefits of robotic maintenance are evaluated. [ABSTRACT FROM AUTHOR]

From the past to the present when robotics world is examined, it is seen that robots have found a wide field of research in many fields such as space, military, medicine, search and rescue besides industrial robots with the breakthroughs realized in hardware and software areas. More detailed and comprehensive studies have requiremented the decompose of robots according to their field of work and the development of planning algorithms that can work in these areas. According to this requirement, the comparison of hardware detection systems and management algorithms according to the usage areas of robots is considered in this article. In this study, application areas, achievements, deficiencies and basic problems of robot systems and areas where research can be done in the future are presented comparatively. [ABSTRACT FROM AUTHOR]

One of the main subjects that is studied in multi-robot applications is allocating tasks to robots and constructing objective-efficient tours for the robots using these allocated tasks. The main purpose of this paper is to allocate tasks via market-based task allocation architecture and to construct collision-free routes for multi-robot systems in a known indoor environment. For task allocation, a market-based architecture is constructed and applied to an example of a team of four heterogeneous mobile robots. In the study, tasks are allocated according to one of two constraints: minimizing the makespan of the robots and maximizing the robot task matching value of each robot in the group. These constraints are achieved by changing the value of a decision parameter. To show the effectiveness of the proposed method, comparisons are made within these constraints. To construct path, a combination of the Nearest Neighbor heuristic and Dijkstra's shortest path algorithms is used. To construct non-conflicting paths, a method that detects and solves collisions is developed. In the study, for agent communication Open Agent Architecture is used. Additionally, simulations on the MobileSim platform are conducted to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, a real-time tracking application of different colored objects with a vision based mobile robot has been implemented. The developed robot has some features that can be used in the fields of exploration, security and observation. This vision based mobile robot is autonomously operated by using image processing and robot vision techniques. The response time of the robot that can track different color (red, blue and green) objects is between 96 ms and 106 ms as suitable for real time processing. By two different developed algorithms, for blue and red color objects a %100, and for green objects a %60 recognition success is seen with experiments. In colorful object tracking experiments in linear and circular orbitals, for an average value of 5.7 cm/s speed, a 4.5 cm deviation as maximum is confirmed. [ABSTRACT FROM AUTHOR]

Because robots are becoming more complex every day, the decision-making system which determines how the robot shall act, also gets equally complicated, becoming more difficult to manage. These difficulties may be overcome by grouping all movements and tendencies of the robot as 'behaviors'. A behavior is a mechanism which describes the reaction and response corresponding to a stimulus. From this perspective, decision-making systems are revised into behaviors and consequently simplified. In this approach, the problems of which behavior to consider and how to combine responses to get an optimum result, are critical factors for attaining success. Response of a behavior is independent of the global purposes of the robot; it always gives the same reaction to the same action. Therefore it should be managed with a higher-level layer so that the robot could accomplish its global purposes. The responses of behaviors are converted into action by evaluation according to a coordination mechanism and then necessary action is taken. The success of the behavioral robot is closely related to the coordination system. The most widely used coordination methods are as follows; (1) Arbitration: The behavior with the highest priority will be executed, (2) Action- Selected: The selected action depending on the conditions and preferences present at the time is executed, (3) Voting: The response winning the majority of the votes is taken into account and the rest is disregarded, (4) Collective: The responses of the active behaviors are collected and the necessary action is executed. The process of combining behaviors involves various types of uncertainties including (1) inputs used to determine responses of the behaviors, (2) rules regarding the type of response given by the "behavior" to these inputs, (3) if more than one behavior is considered, the rules to determine which one of these should be selected as the "dominant behavior." A tool is necessary to represent and process these uncertainties existing in the responses. Uncertainty matrix is able to fulfill this task. A new approach is proposed in this work that coordinates responses of all behaviors using Uncertainty Matrix. In this method, responses of each behavior are represented by Uncertainty Matrix and the combined response of the behavior is calculated. Then the main result is found with the same manner. The navigation problem of behavior-based robots to reach the target point while keeping away from the obstacles was simulated. The simulation environment consists of a 20 m × 30 m sized rectangular platform and one behavioral mobile robot placed in this, one unit of target point, three units of mobile or fixed obstacle blocks with a diameter of 1 m, and one unit of obstacle block with a diameter of 3 m. The robot has four behaviors, namely, move to goal, avoid obstacles, go ahead, and walk around. Two methods of behavior coordination are compared: (1) the classical approach that selects a behavior using behavior selection matrix and (2) the approach proposed in this work that coordinates responses of all behaviors using uncertainty matrix. In the second method, all responses of each behavior are represented by uncertainty matrix and the combined response is calculated.… [ABSTRACT FROM AUTHOR]

With the rapid improvement of computer technology, Visual-based sensors have gained an intense popularity and consequently have begun to be utilized extensively in robotic research. Among the various applications in robotics, one of the most popular concepts is gathering information from the navigation environments for mobile robots by working on optical flow of vision which is derived from a stereo camera located on the robots. We can determine from the optical flow the movement of the objects within the area of robotic vision. If a relative motion in the environment, whether from objects or the mobile robot, is present, then the information that can be gathered from this environment is enough for the mobile robot to execute its obstacle detection and avoidance behaviors. Optical Flow is a concept which has been worked on for quite a long time. But due to problems which prevail on all visual based applications, such as computing difficulties and slow rate of getting results, researchers have come across with so many difficulties that deter them from use in real time applications, especially in robotics. But as the latest research and techniques have come to view, new practical methods were put forward. In this study, by making use of optical flow calculation and multi layer perceptron Artificial Neural Network, a methodology has been tried to be put forward for mobile robot obstacle detection and avoidance behavior. The study of methodology has been supported by experimental results that were obtained from Matlab simulation environments. The images of the views were taken from the real navigation environment and then optical flow calculations for all images were obtained via matlab simulink blocks that were created in advance, as an algorithm which can calculate optical flows from stereo visions. As optical flows of each pair of stereo views were derived, a data base was constituted in order to train the multi layer perceptron. By the help of the data set and the Levenberg- Marquardt learning algorithm, a neural network which was well trained in Matlab environment in order to detect the presence of obstacles was created. Experimental results, obtained during the study have strengthened the ideas which have supported the usage of the Optical Flow via Artificial Neural Network in mobile robotics for obstacle detection and avoidance behaviors. [ABSTRACT FROM AUTHOR]