Mine Detection Sensors Research Articles

Omnidirectional All-Terrain Screw-Driven Robot Design, Modeling, and Application in Humanitarian Demining*

In this paper, we present a screw-propelled omnidirectional mobile robot with the ability to navigate on a variety of terrains. The robot design, kinematic and inverse-kinematic models, and testing on different terrains are presented. In the end it was determined that the robot could operate on most terrains in any direction. To show the potential for the robot in field applications, the robot was used in a simulated humanitarian demining scenario. The robot coupled with a mine detection sensor showed promise for this use.

Autonomous Mobile Robot Navigation System

This project presents a metal detecting robot using RF communication with wireless audio and video transmission and it is designed and implemented with Atmel 89C51 MCU in embedded system domain. The robot is moved in particular direction using switches and the images are captured along with the audio and images are watched on the television .Experimental work has been carried out carefully. The result shows that higher efficiency is indeed achieved using the embedded system. The proposed method is verified to be highly beneficial for the security purpose and industrial purpose. The mine sensor worked at a constant speed without any problem despite its extension, meeting the specification required for the mine detection sensor. It contributed to the improvement of detection rate, while enhancing the operability as evidenced by completion of all the detection work as scheduled. The tests demonstrated that the robot would not pose any performance problem for installation of the mine detection sensor. On the other hand, however, the tests also clearly indicated areas where improvement, modification, specification change and additional features to the robot are required to serve better for the intended purpose. Valuable data and hints were obtained in connection with such issues as control method with the mine detection robot tilted, merits and drawbacks of mounting the sensor, cost, handling the cable between the robot and support vehicle, maintainability, serviceability and easiness of adjustments. These issues became identified as a result of our engineers conducting both the domestic tests and the overseas tests by themselves, and in this respect the findings were all the more practical.

매니퓰레이터를 이용한 지하 매설물 탐지의 효율적 탐지경로에 관한 연구

This paper shows an effective scanning trajectory for a mine detection device that is one of the mission equipments of unmanned ground vehicle. The mine detection device is composed of a mine-detection sensor, and a 4 DOF manipulator enabling sensor position control. There are three modes that manage the mine detection device: passive, semi-automatic, and automatic. The automatic mode is used the most. This paper suggests a scanning method that makes shape of 8. This method prevents missing target area and enhances scanning speed when the mine detection device scans the ground surface in automatic mode. The suggested method is verified by simulations and experiments.

지뢰 탐지를 위한 지면추종 및 탐지위치 표식에 관한 연구

The mine-detection system, which is one of the various mission equipments for Ground Vehicle System, detects mine under the ground. The mine detection sensors comprised of Metal Detection(MD) sensor and Ground Penetration Radar(GPR) are attached on the end of the multi-DOF manipulator. The manipulator moves the sensor to sweep mine areas keeping the pre-determined distance between the sensor and ground to enhance mine detection performance. The detection system can be operated automatically, semi-automatically and manually. When the detection system is operated automatically, the sensor should avoid collisions with unexpected obstacles which may exist on the ground. Two types of ultra-sonic sensors were developed for the mine detection sensor system to keep the appropriate gap between sensor and the ground to avoid the obstacles. Also, mine place marking device was developed.

Development of an articulated mine-detecting manipulator system for mobile robots

This paper presents a servo motor selection technique for a mobile mine detection system which is operated in the field condition. As a first step, mine detection sensor should follow the earth profile while maintaining a constant distance to the earth surface. In order to acquire a reasonable manipulator trajectory and its required torque, a kind of modeling technique is devised to remove the unwanted fluctuations in the earth data. Then, Straete et al.'s normalization method is adopted and is improved to select the adequate actuator for each joint of manipulator. The improvements are as follows: First, an equivalent output power lines are plotted at the same load curve, which gives another selection criterion. Second, a temperature based criterion is proposed for the continuous limit, which estimates the steady state winding temperature of a motor, and can be used to determine a suitable motor and gear ratio. This formulation gives not only the selection of a motor and gear ratio, but also gives the estimated operating temperature. Finally, realized system is verified through the basic performance test.

Design consideration of actuator specifications in a mobile manipulator system

This article presents a servo motor selection technique for a mobile mine detection system, which is operated in the field condition. As a first step, the mine detection sensor should follow the earth profile while maintaining a constant distance to the earth surface. In order to acquire a reasonable manipulator trajectory and its required torque, a kind of modelling technique is devised to remove the unwanted fluctuations in the earth data. Then, Straete et al.'s normalization method is adopted and is improved to select the adequate actuator for each joint of the manipulator. The improvements are as follows: first, an equivalent output power lines are plotted at the same load curve, which gives another selection criterion. Second, a temperature-based criterion is proposed for the continuous limit, which estimates the steady-state winding temperature of a motor, and can be used to determine a suitable motor and gear ratio. This formulation not only gives the selection of a motor and gear ratio, but also gives the estimated operating temperature.

Possibilistic Versus Belief Function Fusion for Antipersonnel Mine Detection

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Two approaches for combining humanitarian mine detection sensors are presented—one based on belief functions and the other one based on possibility theory. The approaches are described in parallel. First, different measures are extracted from the sensor data. Mass functions and possibility distributions are then derived from the measures based on prior information. After that, the combination of masses and the combination of possibility degrees are performed in two steps, on a separate sensor level and between the sensors. Combination operators are chosen to account for different characteristics of the sensors. The selection of the decision rules is discussed for both approaches. The proposed approaches are illustrated on a set of real mines and nondangerous objects, and promising results have been obtained. </para>

Generalized two-sided linear prediction approach for land mine detection

Ground penetrating radar (GPR) is a widely used sensor for land mine detection. However, GPR signal return is very susceptible to ground bounce and reflection of clutter objects, which makes the detection a difficult problem to date. In this paper, we propose to utilize two-sided linear prediction (LP) to model the background interference and then employ the residue energy to generate the test statistic. It is demonstrated from real GPR data that the proposed scheme is able to significantly suppress the interference due to ground reflection and is superior to the adaptive ground bounce removal and one-sided LP methods.

Fiber-optic laser sensor for mine detection and verification

What we believe to be a new optical approach for the identification of mines and explosives by analyzing the surface materials and not only bulk is developed. A conventional manually operated mine prodder is upgraded by laser-induced breakdown spectroscopy (LIBS). In situ and real-time information of materials that are in front of the prodder are obtained during the demining process in order to optimize the security aspects and the speed of demining. A Cr4+:Nd3+:YAG microchip laser is used as a seed laser for an ytterbium-fiber amplifier to generate high-power laser pulses at 1064 nm with pulse powers up to E(p) = 1 mJ, a repetition rate of f(rep.) = 2-20 kHz and a pulse duration of t(p) = 620 ps. The recorded LIBS signals are analyzed by applying neural networks for the data analysis.

Discrimination Mode Processing for EMI and GPR Sensors for Hand-Held Land Mine Detection

Signal processing algorithms for hand-held mine detection sensors are described. The goals of the algorithms are to provide alarms to a human operator indicating the likelihood of the presence of a buried mine. Two modes of operations are considered: search mode and discrimination mode. Search mode generates an initial detection at a suspected location and discrimination mode confirms that the suspected location contains a land mine. Search mode requires that the signal processing algorithm generate a detection confidence value immediately at the current sample location and no delay in producing an alarm confidence is tolerable. Search mode detection has a high false-alarm rate. Discrimination mode allows the operator to interrogate the entire suspected location to eliminate false alarms. It does not require that the signal processing algorithm produce an alarm confidence immediately for the current sample location, but rather allows the system to process all the data acquired over the region before producing an alarm. This paper proposes discrimination mode processing algorithms for metal detectors (MDs), or electromagnetic induction sensors (EMIs), ground-penetrating radars (GPRs), and their fusion. The MD discrimination mode algorithm employs a model-based approach and uses the target model parameters to discriminate between mines and clutter objects. The GPR discrimination mode algorithm uses the consistency of detection as well as the shape of the detection peaks over several sweeps to improve the discrimination accuracy. The performances of the proposed algorithms were examined on a dataset collected at a government test site, and performance was compared with baseline techniques. Experimental results showed that the proposed method can reduce the probability of false alarm by as much as 70% at a 100% correct detection rate and performed comparable to the best human operator on a blind test with data collected at approximately 1000 locations.

Sensor fusion in anti-personnel mine detection using a two-level belief function model

A two-level approach for modeling and fusion of antipersonnel mine detection sensors in terms of belief functions within the Dempster-Shafer framework is presented. Three promising and complementary sensors are considered: a metal detector, an infrared camera, and a ground-penetrating radar. Since the metal detector, the most often used mine detection sensor, provides measures that have different behaviors depending on the metal content of the observed object, the first level aims at identifying this content and at providing a classification into three classes. Depending on the metal content, the object is further analyzed at the second level toward deciding the final object identity. This process can be applied to any problem where one piece of information induces different reasoning schemes depending on its value. A way to include influence of various factors on sensors in the model is also presented, as well as a possibility that not all sensors refer to the same object. An original decision rule adapted to this type of application is proposed, as well as a way for estimating confidence degrees. More generally, this decision rule can be used in any situation where the different types of errors do not have the same importance. Some examples of obtained results are shown on synthetic data mimicking reality and with increasing complexity. Finally, applications on real data show promising results.

Detection of low-metal content objects by evidential fusion of mine detection sensors

The metal detector is the most widely used sensor in humanitarian demining. However, it does not provide enough information for the detection of objects with a low metal content. In this paper, we propose to combine it with other sensors, as investig

Land mine detection

Currently, very little technology is used in real-world demining activities. Active programs by the U.S. Army in both land mine detection sensor development and systems integration are evaluating new technologies, incrementally improving existing technologies, increasing the probability of detection, reducing the false alarm rate, and planning out usable deployment scenarios. Through iterative design/build test cycles, and blind and scored testing at Army mine lanes, steady progress is being made.

An Integrated Robotic System for Antipersonnel Mines Detection

The terrible antipersonnel landmines plague represents a real challenge for the robotics community. The Royal Military Academy of Belgium intends to provide its contribution for solving this problem. The Belgian Hudem project involves different workgroups each focusing on different aspects: data fusion, sensor development and sensor positioning. The Robotics Workgroup develops semi-autonomous mobile systems which carry mine detection sensors. Mechanical, control and location aspects are considered in the different projects. We present in this paper the control and the communication architecture of our robotics detection systems.

Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection

We present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the full benefits of fusion. A novel algorithm for feature-level fusion of noncommensurate, noncoincidently sampled data is described, in which a model is fitted to the sensor data and the model parameters are used as features. Formulations for both feature-level and decision-level fusion are described, along with some practical simplifications. A closed-form expression is available for feature-level fusion of normally distributed data and this expression is used with simulated data to study requirements for sample position accuracy in multisensor data. The performance of feature-level and decision-level fusion algorithms are compared for experimental data acquired by a metal detector, a ground-penetrating radar, and an infrared camera at a challenging test site containing surrogate mines. It is found that fusion of binary decisions does not perform significantly better than the best available sensor. The performance of feature-level fusion is significantly better than the individual sensors, as is decision-level fusion when detection confidence information is also available ("soft-decision" fusion).

Autonomous Underwater Vehicle for Mine Reconnaissance

An autonomous underwater vehicle for mine reconnaissance is currently being developed by the Norwegian Defence Research Establishment. The system will detect and accurately locate mine-like objects through a systematic, autonomous survey of an area of interest. Multibeam echo sounder data from various dummy mine objects have been collected during several sea trials with HUGIN and other platforms. These real sensor data have been used for evaluation of sensor performance and for developing algorithms for automatic object enhancement and detection. The mine reconnaissance concept will be demonstrated early 2001 with a HUGIN vehicle carrying a minimal mine detection sensor configuration consisting of a side-scan sonar and a multibeam echo sounder. The sensor suite, together with the level of adaptive behaviour, will be upgraded in later versions.

Development of chemically assisted dry etching methods for magnetic device structures

There is a strong need for advanced pattern transfer methods for magnetic devices such as magnetic random access memories, sensors for avionics and mine detection, and read/write heads for high density information storage. As the critical dimensions in these devices are decreased, the use of ion milling for pattern transfer presents major obstacles, including sidewall redeposition (which degrades magnetic performance) and poor mask selectivity. Most magnetic materials do not form volatile etch products in conventional reactive ion etching. We have recently found that high density plasmas provide efficient ion-assisted desorption of metal chloride etch products, provided that the etch production formation and removal are balanced by correct choice of ion/neutral ratio. We have completed the survey of plasma chemistries for etching of giant magnetoresistance (GMR) (NiFe, NiMnSb) and collossal magnetoresistance (CMR) (LaCaMnO3,LaSrMnO3,PrBaCaMnO3) materials. The optimum choices are Cl2/Ar for CMR oxides, SF6/Ar for NiMnSb Heusler alloys and either Cl2/Ar or CO/NH3 for GMR multilayers. We have also addressed the issue of postetch cleaning for corrosion prevention, by combining simple water rinsing with in situ plasma cleans involving H2, O2, or F2. Under optimized conditions, there is excellent long-term stability of both the mechanical and magnetic properties of the multilayer structures.