In this paper, we consider the problem of autonomous exploration of unknown environments with single and multiple robots. This is a challenging task, with several potential applications. We propose a simple yet effective approach that combines a behavior-based navigation with an efficient data structure to store previously visited regions. This allows robots to safely navigate, disperse and efficiently explore the environment. A series of experiments performed using a realistic robotic simulator and a real testbed scenario demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission in large open spaces, narrow cluttered environments, dead-end corridors, as well as rooms with minimum exits.
Abstract-When working with mobile robots, a typical task consists in developing simulated tests before going towards the real implementations. Nevertheless, this simulation stage may be very time consuming for setting-up environments and robots. Also, after demonstrating that things worked well in the simulated environment, implementing algorithms in the real robots demands an extra time consuming stage that requires for the programmer to adapt the code for the real connections. Once this is done, the real world problems come to be the core of challenges in the mobile robotics research area. In that way, service-oriented robotics is starting to provide a path for quick simulation and real implementation setups. In this paper, we make use of the Microsoft Robotics Developer Studio (MSRDS) and a MobileRobots Pioneer 3-AT robot for exploring its behavior under different service providers. Experiments are shown for demonstrating simulated and real tests using technologies as: speech recognition, vision, and sensor-based navigation. Also, information about the main functionality of MSRDS, including VPL and SPL, is presented.
Developing an infrastructure for efficiently coordinating a group of autonomous robots has become a challenging need in robotics community. In recent years, the use of teams of coordinated mobile robots has been explored in several application fields such as military, exploration, surveillance, and search and rescue missions. For such fields, the use of multiple robots enable for robustness at mission accomplishment. In this paper we present a service-oriented, distributed architecture for coordinating a set of mobile robots on the way to a common goal. The main design aspects concern the ease of extendibility, scalability, re-usability and integration of rapidly changing robotic hardware and software, and the support of different application domains rather than limiting to specific tasks' requirements and algorithms. We integrate state of the art ideas such as Service-Oriented Robotics in order to achieve novel solutions. We demonstrate working cooperative autonomous operations using multiple robots with time-suitable communications. This work is an early phase of our ultimate goal, which is to have multiple heterogeneous autonomous robots forming an intelligent system that can be able to deal with highly dynamic and challenging environments such as a first responders team in search and rescue missions.
Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured
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