MineSweeper: Not Just a Game Anymore

Cornell MineSweeper is a nonprofit student organization founded in 2006 by Cornell University engineering student Vikas Reddy that integrates robotics technology and humanitarian initiatives. The team, comprised of over 40 highly dedicated Cornell students, is designing practical robotic vehicles—still in the concept stage—to assist with demining efforts worldwide

Automated landmine detection by means of a mobile robot

Tese de doutoramento em Engenharia Electrotécnica (Instrumentação e Controlo) apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraMillions of antipersonnel landmines are left in the ground after past war conicts across many countries. Being functional for more than 50 years they provide a lot of humanitarian and economical problems long after the conict is _nished. Cleaning the existing mine_elds, called humanitarian demining, is required in order to return the large areas of the land to normal use and save the local civilians from the danger. Currently, the only fully trustable solution for this problem is the manual clearance which is itself a very dangerous and slow procedure. Automation of the humanitarian demining may change the situation providing a faster approach which eliminates the participation of humans on the mine_eld. This work is a part of the e_ort toward the development of such approach. Automation of the humanitarian demining meets a lot of technical problems which currently do not have e_ective solutions. This work covers the ones related to the automatic detection of antipersonnel landmines assisted by a mobile scanning platform which carries the landmine detection sensors. The landmine detection approach developed in this work assumes the employing of several nonselective sensors most widely used for landmine detection which include metal detectors, infrared sensors, and ground penetrating radar. The approach has a multi-stage structure and is based on feature-level sensor fusion strategy. This process is understood as a step-by-step reduction of the false alarm rate depending on the quality of the available sensor data. During the _rst stage the sensor data are processed in order to distinguish all objects suspected to be landmines against the background. For this purpose a novel online algorithm was developed. It allows to detect the object during the robot movement and is hardly sensitive to the quality of the sensor data. The consequent stages are performed in order to recognize the landmines among the detected suspicious objects. A number of new classi_cation features were developed in order to perform this recognition. Based on the feature analysis a concept of selective training specially suited for the landmine recognition task was developed. This technique allows to account for the high overlapping of the classes and multimodal distributions of the classi_cation features. Finally, a concept of dominant class was introduced in order to provide high levels of detection rates even in case of poorly separated classes. Being specially designed for the speci_cs of landmine detection the proposed algorithms allow to improve the results. In order to assist the gathering of the sensor data, the problems related to the effective sensor data gathering,path planning and localization of the platform are also addressed. The developed solutions are implemented on the previously created pneumatic scanning platform acting as a prototype demining robot. A number of practical solutions improving the platform localization were developed. The positioning of the robot is based on its odometry, compass and a novel vision system which are combined together by means of a Kalman _lter. The vision system employees a simple CCD camera and is guided by a novel algorithm for the detection and association of natural landmarks found on the ground surface. Finally, considering the landmine detection problem in the scale of mine_eld the problem of the _eld exploration is addressed. Assuming a general case in which the mine_eld may be populated with some obstacles in unknown positions an algorithm for online unknown area coverage was developed. The algorithm guarantees regularity of the robot path necessary for the mapping of sensor data and the safety of the robot by planning its path only inside already covered area. The developed algorithms were implemented in a form of control software for the real platform. Testing of the proposed ideas in simulation and in real conditions (on a test mine_eld) provided promising results showing the perspective of the developed concepts. Based on the experimental results the recommendations for future work are formulated. The automatic landmine detection task raises a number of challenging problems which have connections to other areas of robotics, pattern recognition and control. In this regards the development of the methods proposed in this work was considered in a more broad sense. Thus, the results of this work can be used in the adjacent _elds of robotics: automatic subsurface sensing with online reaction to the found target, pattern recognition in case of poorly distinguished classes, and online unknown area coverage required for cleaning, grass cutting, agriculture, etc

Automated UAS Aeromagnetic Surveys to Detect MBRL Unexploded Ordnance

Unguided Multiple Barrel Rocket Launcher (MBRL) systems are limited-accuracy, high-impact artillery systems meant to deliver barrages of explosive warheads across a wide area of attack. High rates of failure of MBRL rockets on impact and their wide area of ballistic dispersion result in a long-term unexploded ordnance (UXO) concern across large areas where these systems have been deployed. We field tested a newly-developed UAV (unmanned aerial vehicle)-based aeromagnetic platform to remotely detect and identify unexploded 122 mm rockets of the widely-used BM-21 MBRL. We developed an algorithm that allows near real-time analysis, mapping, and interpretations of magnetic datasets in the field and, as a result, rapid identification of anomalies associated with both surfaced and buried MBRL items of UXO. We tested a number of sensor configurations and calibrated the system for optimal signal-to-noise data acquisition over varying site types and in varying environmental conditions. The use of automated surveying allowed us to significantly constrain the search area for UXO removal or in-place destruction. The results of our field trials conclusively demonstrated that implementation of this geophysical system significantly reduces labor and time costs associated with technical assessment of UXO-contaminated sites in post-conflict regions

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

The Journal of Conventional Weapons Destruction Issue 27.2

Updates on recent enhancements to IMAS. Food security and its connection to mine action as it applies to Ukraine. Digital EORE as a small NGO in mine action. A case study on moving beyond do no harm in environmental mainstreaming in mine action. Efforts of JICA and CMAC in fostering South-South cooperation in mine action. UAV Lidar imaging in mine action to detect and map minefields in Angola. Land disputes and rights in mine action. Computer vision detection of explosive ordnance

The Journal of Conventional Weapons Destruction, Issue 24.1 (2020)

Mine Action on the Korean Peninsula Raising the Profile of Mine Action A New Approach to IMAS Compliance Disposal of EO and Environmental Risk Mitigation Explosive Ordnance Risk Education - Measuring Behavior Chang