Security patrolling BOT based on loT

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.

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.

Multi-agent patrolling problem has received growing attention from many researchers due to its wide range of potential applications. In realistic environment, e.g., security patrolling, each location has different visitation requirement according to the required security level. Therefore, a patrolling system with non-uniform visiting frequency is preferable. The difference in visiting frequency generally causes imbalanced workload amongst agents leading to inefficiency. This paper, thus, aims at partitioning a given area to balance agents’ workload by considering that different visiting frequency and then generating route inside each sub-area. We formulate the problem of frequency-based multi-agent patrolling and propose its semi-optimal solution method, whose overall process consists of two steps – graph partitioning and sub-graph patrolling. Our work improve traditional k-means clustering algorithm by formulating a new objective function and combine it with simulated annealing – a useful tool for operations research. Experimental results illustrated the effectiveness and reasonable computational efficiency of our approach.

This paper proposes an effective autonomous task allocation method that can achieve efficient cooperative work by divisional cooperation in multi-agent contexts. Computer and network technology has enabled agents/robots to behave autonomously and to be used in a variety of applications such as cleaning and security patrolling. However, to cover large environments, cooperation and collaboration among several agents are mandatory for efficiency and for the required task quality. However, how agents cooperate is a challenging issue because actual environments are usually complicated and because their own (very uncommon) characteristics. Thus, we first define the continuous cooperative patrolling problem, in which agents split up and move around the environments with the required frequencies that are defined for every location. Then, we extend the previous cooperation method to prompt autonomous and effective division of labor by introducing the negotiation for task (re) allocations. We experimentally show that agents with our method enable effective division and fair allocation by identifying their own responsible locations in a bottom-up manner and that they could achieve considerably improved results compared with those of the previous method. We also investigated the structure of the resulting regime for cooperation and analyzed why our method could achieve the effective task allocation.

The coordinated management of inter-dependent plans or schedules belonging to different agents is a complex, real-world problem arising in diverse domains such as disaster rescue, small-team reconnaissance, and security patrolling. The problem is inherently a distributed one; no single agent has a global view and must make local scheduling decisions through collaboration with other agents to ensure a high quality global schedule. A key step towards addressing this problem is to devise appropriate distributed representations. The Coordinators Task Analysis Environmental Modeling and Simulation (C_tæms) language is a representation that was jointly designed by several multi-agent systems researchers explicitly for multi-agent task scheduling problems [1,2,3,4]. C_tæms is an extremely challenging class of scheduling problem which is able to model the distributed aspects of the problem.KeywordsSchedule ProblemTask ScheduleConstraint PropagationTask GroupFeasible ScheduleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the acceleration of information and the rapid development of economy, the management of various industries in the society will tend to be intelligent, information and standardization. There are various new technologies speed up the pace of security management. In this paper, we adopted sensing layer data acquisition technology of Internet of things based on RFID to design an intelligent security patrolling system of the police robots. Meanwhile, we put forward RFID core technology and optimized cryptography technology, which are used to solve the reliability and integrity verification requirements of data acquisition by nodes of the sensing layer of Internet of things.