Hybrid deliberative/reactive control of a scanning system for landmine detection

Antipersonnel mines infest fields all over the world. According to recent estimates, landmines are killing and maiming more than 2000 innocent civilians per month. The problem of landmine detection and removal requires the cooperation of a number of engineering fields, which in turn poses a need for new technologies, such as improved sensors, efficient manipulators and mobile robots. This paper describes the configuration and control architecture of a scanning manipulator for detecting antipersonnel landmines. The scanning system is part of a demining system based on a walking robot that acts as the carrier for the scanning manipulator. Broadly speaking, the scanning system consists of a sensor head that can detect certain kinds of landmines and, to move the sensor head over large areas, a manipulator that has been appropriately sensorized to scan irregular terrains in the presence of obstacles. The proposed control architecture is of the hybrid deliberative/reactive type: A deliberative controller defines a sweep trajectory that furnishes complete coverage of the infested area, while two reactive controllers are involved in on-line adaptation to the environment. Experiments show good performance of the whole system.     

DYLEMA: Using walking robots for landmine detection and location

Detection and removal of antipersonnel landmines is an important worldwide concern. A huge number of landmines has been deployed over the last twenty years, and demining will take several more decades, even if no more mines were deployed in future. An adequate mine-clearance rate can only be achieved by using new technologies such as improved sensors, efficient manipulators and mobile robots. This paper presents some basic ideas on the configuration of a mobile system for detecting and locating antipersonnel landmines efficiently and effectively. The paper describes the main features of the overall system, which consists of a sensor head that can detect certain landmine types, a manipulator to move the sensor head over large areas, a locating system based on a global-positioning system, a remote supervisor computer and a legged robot used as the subsystems’ carrier. The whole system has been configured to work in a semi-autonomous mode with a view also to robot mobility and energy efficiency.     

A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection

We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The system has three sensor types: a metal detector (MD), an infrared camera (IR), and a ground penetrating radar (GPR). The output of the sensors is processed to produce confidence levels on a grid covering the test-bed. A confidence level expresses a confidence or belief in a landmine detection on a certain position. The grid with confidence levels is the input for the decision-level sensor-fusion and provides a co-registration of the sensors. The applied fusion methods are naive Bayes' approaches, Dempster–Shafer theory, fuzzy probabilities, a rule-based method, and voting techniques. To compare fusion methods and to analyse the capacity of a method to separate landmines from the background on the basis of the output of different sensors, we provide an analysis of the different methods by viewing them as discriminant functions in the sensor confidence space. The results of experiments on real sensor data are evaluated with the leave-one-out method.     

Detecting and locating landmine fields from vehicle- and air-borne measured IR Images

Air- and vehicle-borne sensor-based technique is a potentially attractive approach for fast detecting landmines and locating landmine fields towards humanitarian demining. For images measured from airborne and vehicle-borne cameras, landmines may be indicated by direct or indirect signs, e.g., spatial difference from their surroundings due to digging or, due to thermal and material signatures. The background in images usually consists of various types of noise and clutter, e.g., thermal noise, sand, gravel road and vegetation, thus making the detection even more difficult. This paper is focused on the following aspects: (1) Finding a robust detector that is suitable for detecting/locating landmine candidates and man-made landmarks by using infrared images measured from vehicle- or air-borne sensors; (2) Interpreting the detector using the 2D isotropic bandpass filter, matched filter, detection theory and thermodynamic-based landmine models; (3) Extending the detector to a multiscale version where landmine detectability is enhanced by automatically selecting a proper scale and localization is improved by inter-scale position tracing. We propose a special type of isotropic feature detector that exploits the characteristic difference between landmines and their surroundings in the spatial-frequency domain under the multiscale framework. Experiments were performed on several infrared images measured from vehicle-borne sensors as well as airborne sensors on a helicopter over the test bed scenarios. The performance of the detector was also evaluated in terms of detectability, localization, and automatic scale selection of the detector. These results and evaluations have shown the effectiveness of the method and its potential in landmine field detection.     

地铁车辆障碍物检测技术应用研究

障碍物检测是确保地铁车辆运行安全的一个重要措施,但是目前地铁运行障碍物检测存在场站环境下单一传感器检测速度慢、准确性差、范围小的问题,为实现对其的有效弥补,在可见光与近红外结合的双目视觉技术迅速发展的支持下,基于红外成像系统与可见光成像系统,设计地铁障碍物检测系统;基于对系统功能需求与运行平台的分析,明确系统功能模块及其硬件构成;由于红外与可见光图像传感器的成像机理不同,拍摄的图像也存在差异,利用BEEMD算法对图像进行融合之后,执行YOLO障碍物类别检测任务;最后分析系统应用,结果表明可以实现列车运行中前方列车80~500 m安全限界范围内的障碍物识别。     

Robotics-assisted demining with Gryphon

The threat and consequences of landmines have led to a multitude of alternative research activities in the field of demining. While mine sensor-focused research has been intensive, there has been relatively less attention given to the problem of automating the detection and removal procedure. Understandably, autonomous robot operation and interaction in unstructured field environments are difficult. This paper addresses this by presenting a robot meant to assist humanitarian demining by providing a cheap, fast, reliable and safe alternative to human deminers risking their lives on a daily basis. The robot, named Gryphon, is able to autonomously scan 2 m² at a time with any type of mine sensor payload. It then presents acquired sensor images to the operator who selects which spots need further investigation or prodding. The robot then appropriately marks the terrain with paint or marking plates. Gryphon has been extensively field tested in Japan, Croatia and Cambodia.     

Design and performance of a micro-sized biomorphic compound eyewith a scanning retina

Proposes a design of a biomorphic micro visual sensor equipped with a mechanical scanning system. The sensor is inspired by the structure of the fly's compound eye, which was found to possess a unique muscle and tendon actuation system for its scanning retina. The improvement in micro-opto-electro-mechanical systems encourages the design of reliable, efficient, and integrated smart visual sensors for robotic applications, in particular, for the autonomous visually guided navigation of mobile robots. The authors have verified the effect of retinal scanning for a robotic visual system through the construction of a large-scale prototype at the millimeter scale. The performance of a newly fabricated microelectromechanical system prototype whose size approaches the order of the insects' compound eyes is then evaluated in comparison with that of the large-scale prototype. The micro-sized visual sensor is composed of a scanning microlens array (120 μm in lens diameter) and a photo-diode array. The actuation of the microlens array induces a rotation of the visual axes. According to the principle of retinal scanning, the micro-sensor is able to retrieve the local angular velocity with enhanced reliability     

Degree of familiarity ART2 in knowledge-based landmine detection

The self-organizing network ART2 is extended to provide a fuzzy output value, which indicates the degree of familiarity of a new analog input pattern to previously stored patterns in the long-term memory of the network. The outputs of the multilayer perceptron and this modified ART2, provide an analog value to a fuzzy rule-based fusion technique which also uses a processed polarization resolved image as its third input. In real-time situations these two classifier outputs indicate the likelihood of a surface landmine target when presented with a number of multispectral and textural bands. Due to the modifications in ART2 this updated alternative architecture has improved real-time landmine detection capabilities although the registration of all bands is more critical to the accuracy of results in this case. The real-time fuzzy rule-based system in preliminary tests has detected two of the three landmines and the landmine surrogate with two false alarms. Advanced tests on 30 images using the fuzzy rule-based system further confirmed the distinct advantages of fusion and improved detection rates.     

A Modular Concept of the Robotic Vehicle for Demining Operations

Paper analyses some most important characteristics that should be taken into consideration in building the robotic demining vehicle. Based on previous experiences from the development of demining technology the modular concept of the multipurpose vehicle and some its main functional parts are discussed. Such robotic vehicle can be used as general porter of various detection systems, tools for cleaning terrain as well as neutralization equipment. Further development towards partially autonomous system and some principal tasks of positioning in dangerous terrain are analyzed. The real construction of the vehicle equipped by the flailing mechanisms for mechanical activation of explosions is briefly presented.tefan Havlk graduated in Mechanical and in Control engineering and received the (M.S.) degree from Czech Technical University, Liberec (1972). In 1982 he received the Ph.D. degree and the highest scientific degree Dr.Sc. (1994) from the Slovak Academy of Sciences, the scientific institution, where he works since 1977.Within years 1991 and 1992 he has been appointed as invited professor at Swiss Federal Institute of Technology (EPFL-DMT/IMT) in Lausanne, Switzerland. Currently he is head of the research department at the Institute of Informatics of the Slovak Academy of Sciences in Banska Bystrica, Slovakia.– His research activities diverge from solving problems of advanced robotics, control to applications. His main contributions are oriented to the following topics:– Mechatronic design and flexible structures– Sensors and sensory equipment– Applications of advanced robotics: precise assembly and welding, service operations– Robotic tools for demining.He is author more then 100 scientific papers in books, international scientific journals and conference proceedings. He was leading several research projects oriented to development advanced sensing and robotic systems for manufacturing (arc welding, assembly) or for humanitarian demining.He is/was member of several professional organizations and committees under IARP, IMECO, IFAC or IFToMM.     

Continuous free-crab gaits for hexapod robots on a natural terrain with forbidden zones: An application to humanitarian demining

Autonomous robots are leaving the laboratories to master new outdoor applications, and walking robots in particular have already shown their potential advantages in these environments, especially on a natural terrain. Gait generation is the key to success in the negotiation of natural terrain with legged robots; however, most of the algorithms devised for hexapods have been tested under laboratory conditions. This paper presents the development of crab and turning gaits for hexapod robots on a natural terrain characterized by containing uneven ground and forbidden zones. The gaits we have developed rely on two empirical rules that derive three control modules that have been tested both under simulation and by experiment. The geometrical model of the SILO-6 walking robot has been used for simulation purposes, while the real SILO-6 walking robot has been used in the experiments. This robot was built as a mobile platform for a sensory system to detect and locate antipersonnel landmines in humanitarian demining missions.     

Landmine detection research pushes forward, despite challenges

The paper discusses technology for landmine detection, including: detection sensors; Comet II six-legged walking mine detection robot; a multisensor approach in the Hudem project; and the Swarm concept for mini robots that operate like insects. Although numerous basic research projects are in progress, integrating the work into a testable system is itself a significant challenge. At CCMAT, promising technologies that are field ready are tested and evaluated at DRES for mine detection effectiveness.     

The impact of false alarm mitigation on surface landmine detection in MWIR imagery

Surface landmine and minefield detection from airborne imagery is a difficult problem. As part of the minefield detection process, anomaly detection is performed to identify potential landmines in individual airborne images. Post-processing is performed on the initial landmines identified to reduce the number of false alarms, referred to as false alarm mitigation. In this research, a circular harmonics transform image processing approach (the CHT method) and a constant false alarm rate technique (the RX approach) are investigated for surface landmine detection and false alarm mitigation in medium wave infrared (MWIR) image data. The false alarm mitigation approach integrates the CHT and RX methods to identify candidate landmine locations with one technique at a given false alarm rate and applies the other technique to confirm landmine locations and eliminate potential false alarms. Individual detector and false alarm mitigation experimental results are presented for 31 daytime and 43 nighttime MWIR images containing 76 and 142 landmines, respectively. At a 0.9 desired probability of landmine detection, experimental results show that false alarm mitigation reduces the false alarm rate by as much as 84.3% and 13.7% for daytime and nighttime images, respectively, maintaining the probability of detection at 0.85 and 0.90, respectively.     

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.     

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

Information sharing across multiple humanitarian organizations—a web-based information exchange platform for project reporting

This article analyzes information sharing problems in the humanitarian development sector and proposes the concept of a web-based exchange platform to face some of the technical challenges. The “Development Information Exchange System” is a mediator-wrapper-architecture that uses XML documents to loosely couple autonomous and heterogeneous information systems. Detailed project information of humanitarian organizations that resides on data provider systems can be formatted with XSL stylesheets according to the needs of the users and shared within or between organizations. The system can help to close the control loop by providing qualitative information about humanitarian projects. This makes project management more efficient. The proposed architecture solves an interface problem between the various partners and stakeholders of humanitarian projects. It is a first step towards a service-oriented architecture between humanitarian organizations. The next step could be the definition of cross-organizational business processes. These processes may be defined platform-independently with the Business Process Execution Language for Web Services. A prototype of the exchange platform is presented and evaluated in this article.     

Robust landmine detection from thermal image time series using Hough transform and rotationally invariant features

ABSTRACT

Automated detection of buried anti-personnel landmines using remote sensing techniques is very important for clearing minefields without putting lives in danger. Although thermal infrared imaging is promising, it is far from applicable to the real world in its current state-of-the-art. The most serious problem is that experiments are generally held using sandboxes or levelled and cleared soil, but real fields are, at least partially, covered with plants. In this study, we present an algorithm for landmine detection that is robust enough to detect beyond the clutter caused by partial plant cover. The first part is a hypothesis generator based on circular Hough Transform applied to images that are filtered to enhance circular structures. The second part tests the candidate landmine coordinates using rotationally invariant features, including modified Histogram of Oriented Gaussians (HOG), over multiple images taken at different times after Wiener filtering to maximize signal-to-clutter ratio. The performances of various features and classifiers are compared. The overall performance of the algorithm is demonstrated on a dataset of real-world landmine images contaminated by simulated plants. Satisfactory results are obtained up to 40% equivalent plant coverage where more than 65% of the pixels are fully or partially covered by plants.     

Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans

Under study is an application of Ground Penetrating Radar (GPR) to landmine detection problem. We focus on the detection of antitank mines carried out in the 3D GPR images, so-called C-scans, by means of a machine learning approach. In that approach, we particularly pursue a technique for fast extraction of image features based on an initial calculation of multiple integral images. This allows later to calculate each feature in constant time, regardless of the scanning window position and size. The features we study are statistical moments formulated in their 3D variant. We present a comparison of detection results for different sizes and parameterizations of feature sets. All results are obtained from a prototype GPR system of our original construction in terms of both hardware and software.     

用于避障研究的微型仿生机器鱼3维仿真系统

基于VC++6.0开发环境和OpenGL（open graphics library）国际图形标准,在Windows系统下开发了微型仿生机器鱼3维仿真系统。该系统可以降低用实体机器鱼进行机器鱼避障能力研究的成本和减少在研究过程中对实体机器鱼造成的损害。采用多边形建模的方法构建了虚拟微型仿生机器鱼模型,模拟了鱼类尾鳍的摆动。提出了一种模拟红外传感器探测障碍物的虚拟射线方法。并采用实时模糊决策算法设计了基于多传感器信息的复合模糊控制器,决策微型仿生机器鱼的避障行为。仿真实验表明,复合模糊控制器实时性好、效率高;无论是单个任意形状的障碍物还是多个连续障碍物,复合模糊控制器都能有效地引导仿生机器鱼避开障碍物,到达目标点。微型仿生机器鱼3维仿真系统为研究仿生机器鱼的自主避障能力提供了可靠、逼真、便利的平台。     

Introducing the use of depth data for fall detection

Current emergency systems for elderly contain at least one sensor (button or accelerometer), which has to be worn or pressed in case of emergency. If elderly fall and loose their consciousness, they are not able to press the button anymore. Therefore, autonomous systems to detect falls without wearing any devices are needed. This paper presents three different non-invasive technologies: the use of audio, 2D sensors (cameras) and introduces a new technology for fall detection: the Kinect as 3D depth sensor. Our fall detection algorithms using the Kinect are evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living. The evaluation results are compared with State-of-the-Art approaches using 2D sensors or microphones.