Percepcija u inteligentnim prostorima: kombinirana primjena distribuiranih i robotskih senzora

This work considers the joint use of robot onboard sensors and a network of sensors distributed in the environment for tracking the position of the robot and other objects. This is motivated by our research on Intelligent Spaces, which combine the use of distributed sensors with mobile robots to provide various services to users. Here we analyze the distributed sensing using the extended information filter and computation issues that arise due to correlations between estimates. In turn we show how the correlations can be resolved with the use of Covariance Intersection at a cost of conservative estimates, and analyze two special cases where the issues related to correlations can be reduced.Ovaj rad razmatra kombiniranu primjenu senzora na mobilnim robotima i mreže senzora distribuiranih u prostoru za praćenje položaja robota i ostalih objekata. Rad je dio istraživanja o "inteligentnim prostorima", gdje se koriste distribuirani senzori i mobilni roboti sa svrhom pružanja različitih usluga korisnicima prostora. Analizirana je upotreba proširenog informacijskog filtra za distribuiranu percepciju te računski problem uzrokovan korelacijama u procesu estimacije. Potom je objašnjeno rješenje problema korelacija korištenjem metode presjeka kovarijanci (Covariance Intersection), koje međutim daje konzervativne rezultate, te je dana analiza dva specijalna slučaja kod kojih je moguće ublažiti utjecaj korelacija

Analisis Perhitungan Laser Range Finder Menggunakan Persamaan Geometri Pada Sistem Keamanan Ruangan

Kamera pengawas saat ini sudah sangat lazim digunakan, tidak hanya memantau tapi juga dapat memberikan rasa aman bagi penggunanya. Penelitian ini membahas akurasi perhitungan jarak laser memakai persamaan geometri pada webcam yang diterapkan untuk keamanan ruangan. Persamaan geometri digunakan untuk menghitung nilai parameter cahaya laser yang ditangkap webcam. Sinar laser yang diarahkan pada objek tertentu dengan kondisi jarak antara laser dan webcam 6 cm memiliki tingkat akurasi paling bagus pada jarak 22 cm, 40 cm dan 80 cm dengan rata-rata error sebesar 0,11%. Aplikasi sistem ini kemdian digunakan sebagai parameter untuk mendeteksi adanya penyusup pada sebuah ruangan kosong.Kata Kunci—: geometri, jarak, keamanan ruangan, laser , webcam

Wireless Sensor Networks for Detection of IED Emplacement / 14th ICCRTS: C2 and Agility

14th International Command and Control Research and Technology Symposium (ICCRTS), June 15-17, 2009, Washington DC.This paper appeared in the Proceedings of the 14th International Command and Control Research and Technology Symposium, Washington, DC, June 2009.We are investigating the use of wireless nonimaging-sensor networks for the difficult problem of detection of suspicious behavior related to IED emplacement. Hardware for surveillance by nonimaging-sensor networks can cheaper than that for visual surveillance, can require much less computational effort by virtue of simpler algorithms, and can avoid problems of occlusion of view that occur with imaging sensors. We report on four parts of our investigation. First, we discuss some lessons we have learned from experiments with visual detection of deliberately-staged suspicious behavior, which suggest that the magnitude of the acceleration vector of a tracked person is a key clue. Second, we describe experiments we conducted with tracking of moving objects in a simulated sensor network, showing that tracking is not always possible even with excellent sensor performance due to the illconditioned nature of the mathematical problems involved. Third, we report on experiments we did with tracking from acoustic data of explosions during a NATO test. Fourth, we report on experiments we did with people crossing a live sensor network. We conclude that nonimaging-sensor networks can detect a variety of suspicious behavior, but implementation needs to address a number of tricky problems.supported in part by the National Science Foundation under the EXP Program and in part by the National Research Council under their Research Associateship Program at the Army Research Laborator

The Android Smartphone as an Inexpensive Sentry Ground Sensor

Proc. SPIE Conf. on Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV, Baltimore, MD, April 2012A key challenge of sentry and monitoring duties is detection of approaching people in areas of little human traffic. We are exploring smartphones as easily available, easily portable, and less expensive alternatives to traditional military sensors for this task, where the sensors are already integrated into the package. We developed an application program for the Android smartphone that uses its sensors to detect people passing nearby; it takes their pictures for subsequent transmission to a central monitoring station. We experimented with the microphone, light sensor, vibration sensor, proximity sensor, orientation sensor, and magnetic sensor of the Android. We got best results with the microphone (looking for footsteps) and light sensor (looking for abrupt changes in light), and sometimes good results with the vibration sensor. We ran a variety of tests with subjects walking at various distances from the phone under different environmental conditions to measure limits on acceptable detection. We got best results by combining average loudness over a 200 millisecond period with a brightness threshold adjusted to the background brightness, and we set our phones to trigger pictures no more than twice a second. Subjects needed to be within ten feet of the phone for reliable triggering, and some surfaces gave poorer results. We primarily tested using the Motorola Atrix 4G (Android 2.3.4) and HTC Evo 4G (Android 2.3.3) and found only a few differences in performance running the same program, which we attribute to differences in the hardware. We also tested two older Android phones that had problems with crashing when running our program. Our results provide good guidance for when and where to use this approach to inexpensive sensing

Analisis Perhitungan Laser Range finder Menggunakan Persamaan Geometri Pada Sistem Keamanan Ruangan

Kamera pengawas saat ini sudah sangat lazim digunakan, tidak hanya memantau tapi juga dapat memberikan rasa aman bagi penggunanya. Penelitian ini membahas akurasi perhitungan jarak laser memakai persamaan geometri pada webcam yang diterapkan untuk keamanan ruangan. Persamaan geometri digunakan untuk menghitung nilai parameter cahaya laser yang ditangkap webcam. Sinar laser yang diarahkan pada objek tertentu dengan kondisi jarak antara laser dan webcam 6 cm memiliki tingkat akurasi paling bagus pada jarak 22 cm, 40 cm dan 80 cm dengan rata-rata error sebesar 0,11%. Aplikasi sistem ini kemdian digunakan sebagai parameter untuk mendeteksi adanya penyusup pada sebuah ruangan kosong. Kata Kunci—: geometri, jarak, keamanan ruangan, laser , webcam

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

LeaF: A Learning-based Fault Diagnostic System for Multi-Robot Teams

The failure-prone complex operating environment of a standard multi-robot application dictates some amount of fault-tolerance to be incorporated into every system. In fact, the quality of the incorporated fault-tolerance has a direct impact on the overall performance of the system. Despite the extensive work being done in the field of multi-robot systems, there does not exist a general methodology for fault diagnosis and recovery. The objective of this research, in part, is to provide an adaptive approach that enables the robot team to autonomously detect and compensate for the wide variety of faults that could be experienced. The key feature of the developed approach is its ability to learn useful information from encountered faults, unique or otherwise, towards a more robust system. As part of this research, we analyzed an existing multi-agent architecture, CMM – Causal Model Method – as a fault diagnostic solution for a sample multi-robot application. Based on the analysis, we claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors. However, the analysis also showed that the CMM method in its current form is incomplete as a turn-key solution. Due to the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Therefore, based on these preliminary studies, we designed an alternate approach, called LeaF: Learning based Fault diagnostic architecture for multi-robot teams. LeaF is an adaptive method that uses its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. LeaF combines the initial fault model with a case-based learning algorithm, LID – Lazy Induction of Descriptions — to allow robot team members to diagnose faults and to automatically update their causal models. The modified LID algorithm uses structural similarity between fault characteristics as a means of classifying previously un-encountered faults. Furthermore, the use of learning allows the system to identify and categorize unexpected faults, enable team members to learn from problems encountered by others, and make intelligent decisions regarding the environment. To evaluate LeaF, we implemented it in two challenging and dynamic physical multi-robot applications. The other significant contribution of the research is the development of metrics to measure the fault-tolerance, within the context of system performance, for a multi-robot system. In addition to developing these metrics, we also outline potential methods to better interpret the obtained measures towards truly understanding the capabilities of the implemented system. The developed metrics are designed to be application independent and can be used to evaluate and/or compare different fault-tolerance architectures like CMM and LeaF. To the best of our knowledge, this approach is the only one that attempts to capture the effect of intelligence, reasoning, or learning on the effective fault-tolerance of the system, rather than relying purely on traditional redundancy based measures. Finally, we show the utility of the designed metrics by applying them to the obtained physical robot experiments, measuring the effective fault-tolerance and system performance, and subsequently analyzing the calculated measures to help better understand the capabilities of LeaF

Geomatics Applications to Contemporary Social and Environmental Problems in Mexico

Trends in geospatial technologies have led to the development of new powerful analysis and representation techniques that involve processing of massive datasets, some unstructured, some acquired from ubiquitous sources, and some others from remotely located sensors of different kinds, all of which complement the structured information produced on a regular basis by governmental and international agencies. In this chapter, we provide both an extensive revision of such techniques and an insight of the applications of some of these techniques in various study cases in Mexico for various scales of analysis: from regional migration flows of highly qualified people at the country level and the spatio-temporal analysis of unstructured information in geotagged tweets for sentiment assessment, to more local applications of participatory cartography for policy definitions jointly between local authorities and citizens, and an automated method for three dimensional (3D) modelling and visualisation of forest inventorying with laser scanner technology