Landmines Detection Using Low-Cost Multisensory Mobile Robot

Landmines Detection Using Low-Cost Multisensory Mobile Robot

Ahmed Ismail1, Mohammed Elmogy2, and Hazem ElBakry3 1Information Systems Dept., Faculty of Computers and Information, Mansoura University,

Egypt [email protected]

2Information Technology Dept., Faculty of Computers and Information, Mansoura University, Egypt

[email protected] 3Information Systems Dept., Faculty of Computers and Information, Mansoura University,

Egypt [email protected]

Abstract

Landmines are dangerous military equipments that threat people’s lives and cause economic problems. They are harmful because of their unknown locations and difficulty to detect. Detecting and clearing mines demand special expertise. In this paper, we present a low-cost system for landmines detection. We describe the strategy that can help to detect mines using a multisensor robot and path planning algorithm for searching mines. We present how the robot can get information from different sensors to guide soldiers to detect landmines. The purpose is to give an efficient solution of the landmines problem by using a tele mobile robots that are capable of exploring and destroying buried landmines. The proposed robot is using sensor fusion technique to increase the probability of mine detection. We developed decision level fusion to decrease false alarms.

Keywords: Landmine Detection, Low Cost Robot, Multi-Sensor Devices, Sensor Fusion, Motion Planning, Mining Detection Strategies, Mining Defusing.

1. Introduction

There are two kinds of landmines: Anti-Personnel (AP) mines and Anti-Tank (AT) mines. AP mines usually located under the earth and close to the surface while AT mines are typically located on the surface of the earth. Landmines are denial weapons that are used to prevent the direct attack or to deny access to military and civilians to a particular area. Landmines are long-time killers, and they are active long after a war has ended. Now, there are about 100 million of undetected landmines in more than 50 countries [3]. It means that mines kill tens of people every day in locations where they exist [13]. To clear up landmines, two main steps used to be done. The first step is to detect mines position. The second step is to destroy the mines. Employing a robot in mines detection will help in ensuring the safety of those who worked in the minesweeping.

Recently, several attempts have been considered to reduce the risk. Remote sensing or what so called vehicle-mounted sensors is considered more appropriate for robotic applications because it is safer and more efficient. This method is usually used to provide a safe route to the soldiers through minefields. It is common nowadays that landmines detecting is done by a robot that can autonomously detect buried landmines in the same manner of a human operator.

The purpose of this paper is to design a multi-sensor system that collects readings from its surroundings for detecting and defusing landmines in landmines field. The suggested system uses coverage based path planning for real-time obstacle avoidance and to ensure that the robot can demine every position in the landmine field. Moreover, our system determines the position of the mine using sensor fusion system of the multi-sensor to decrease false alarms of using only one sensor alone. Also, our system proposed a defusing system that defuse mines where they are, instead of relocating them.

The rest of the paper is organized as follows. In Section 2, related work is presented. Section 3 discusses the architecture of the proposed robot and the motion-planning algorithm. In Section 4, the importance of using sensor fusion systems in mines detection is discussed. Experimental Results are presented in Section 5. In Section 6, the conclusion and future work are presented.

Landmines Detection Using Low-Cost Multisensory Mobile Robot Ahmed Ismail, Mohammed Elmogy, and Hazem ElBakry

Journal of Convergence Information Technology(JCIT) Volume10, Number6, November 2015

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2. Related Work Different technologies are employed with the aim of detecting mines and determining their locations

efficiently [1]. At present, the effectiveness of landmines detection process depends on the sensor technology and on the motion of vehicles that carry these sensors. Our system combined both methods and thus allowed the operator to stay at a safe distance during crossing minefield. Mines detection technologies use different principles. The first principle sends energy into the ground and reflects off the land mine [9]. Using differences in the wavelength of the spatial resolution of buried mines to detect mines. The second principle is based on receiving energy that is emitted by the explosive inside the landmine [6]. Therefore, this method needs that the sensor must be very close to the soil. The third technology is based on acoustic, or seismic energy reflected from the target [9]. The acoustic technique operates by sending an acoustic or seismic wave to the ground through a seismic source. This approach depends on the amount of objects in the soil.

The proposed system used the three ways together by using data fusion of multi-sensor system to avoid the challenges of using one technology of them alone. Fusion is a technique in which information from several detection systems becomes relevant. The output information is grouped and compared with different technologies to get accurate decision and to avoid the weaknesses of each. Multisensor fusion in landmine detection states the difference between data fusion and data integration. With respect to data fusion, a multi-system includes three main levels: raw data level, vector(feature) level, and the decision level. The data-level technique collects raw data (signals) by each sensor while the feature-level technique gathers information from the sensor about informative "features" extracted from the raw signals. The decision level collects signals from each sensor, and each single does not effect on any other signals. The feature and data-level fusion are used when the decision is superior to fusion at the higher decision level.

The lower-level fusion operates as an integrated unit, presenting the user with a single signal in a manner similar to a single sensor. The feature level fusion is more efficient if there are an array of sensors. Each of them are used to detect a particular type of target. Data or feature-level fusions are more difficult, less mature, and thus far has decision-level fusion. The feature and data level fusions are new research areas. Therefore, it is unknown what degree their theoretical advantages over decision-level fusion in practical applications.

There are many researchers who proposed fusion systems to detect landmines. Gunatilaka [17] proved that using EMI, GPR, and infrared sensors together is better in the result than using the best individual sensor. Collins [16] had shown that the fusion of three sensors EMI, GPR, and magnetometer signals depending on the false alarm is better than raw data of individual sensors. Institute for Defense Analyses (IDA) proved that in a minefield of five vehicle-mounted systems mounted on metal detectors, GPR, and infrared sensors conducted by the fusion of the three signals more better than any single sensor or pair of sensors.

We used in our search the same approach of Shoemaker et al. [7] in decision level. We used in our system low-cost sensors metal detector sensor, a chemical sensor, and ultrasonic sensors, whereas they used infrared camera, ground-penetrating radar, and metal detector to use decision-level sensor fusion. The raw data of the sensors are processed to get data on a reference grid. Shoemaker et al. proved that using decision level helped in decreasing false alarm in mines detection. They divided the mines field into a set of areas, each of them is 50 by 50 cm. They treated them according to the number of mines and used a different threshold to count false alarm and how decreasing it.

On the other hand, there are many researchers who proposed motion planning techniques to detect landmines accuracy. Acar et al. [14] had tested some path planning techniques using robotics. The first technique is using sensor based coverage. The robot incrementally used the coverage algorithm that it is covering space with back and forth motions. The second technique, which is using the probabilistic method, is used when the time is limited, and there exists a priori information about the minefield. The probabilistic method works by minefield parameter extraction. When the parameters are determined, then minefield targets are fixed, allowing opportunistic robot guidance to decrease demining time.

Zhang et al. [20] proposed a probabilistic method for mines detection. They focused on optimization search strategy determining the location of mines and/or unexploded ordnance. They extract the characteristics of dispersion pattern of the minefield to construct a probability map and then design a path for the robot searching.


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3. Low-cost Robot for Landmine Detection Our robot has a four-wheeled all-wheel drive train, which provides a good traction in all terrain

conditions. The conceptual design of the demining robot began with evaluating several existing mechanisms for outdoor navigation. We considered advantages and disadvantages of locomotion exhibited by robots. A simple differential drive robot that uses IR signals to provide support. A simple and cost-effective design that is easy to manufacture and implement, low weight, low power, and small size. As shown in Figure 1, our robot is a differential drive robot that uses inexpensive materials.

Fig. 1: The final assembly of the miner robot.

The robot has four pneumatic wheels and it is mounted on Arduino board. We used Arduino

mega2560 board to handle transfering data from multisensors. We connected the board with metal detector, chemical, camera, ultrasonic, and wireless sensors, as shown in Figure 2. We used wireless module serial UART (200M Range-433 MHz) to communicate with the robot and the supervisor’s laptop. As shown in Figure 3, our demining system is concerned with some main factors, such as, minefield conditions that depend on the environment conditions, and weather conditions and mine conditions. Mine condition, position, and type can affect the robot motion and sensors false alarm.

We designed our robot with four wheels so it gives more stability. We used camera sensor to detect the whole environment in front of the robot. We used an ultrasonic sensor to detect obstacles position and used multisensor to decrease false alarm caused by mine conditions.


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Fig. 2: The proposed demining robotic system structure.

Fig. 3: The block diagram for mining robot.


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3.1 Positioning Our robot has a high-precision, low-cost, and low range external positioning device. We used an

ultrasonic sensor that can produce an accuracy of 0.3 cm in spaces up to 10 meters. It has been tested on a variety of surfaces, including being taken outside and tested on grass. In addition to the level of accuracy, it assumes that all obstacles in the range are less than 10 meters.

On the other hand, we developed a vision system where the robot looked at engineered landmarks to determine its position. The benefit of this approach is that we can localize the robot at great distances, about 20 meters by 20-meters field.

3.2 Motion Planning

The robot is aimed to detect the buried mines, destroy them, and then go on looking for more mines.

When the robot detects a mine, the robot sends out signals back to the operator to the wireless sensor. The robot starts from the beginning of the field to the end of the field. The camera sensor sends the streaming video of the field to the supervisor across the wireless sensor. We used in our search coverage-based algorithm, which is a path planning technique where the robot mainly passes over all points in the minefield at least once to test exactly where mines are.

Demining requires complete coverage of a minefield to locate all the mines. If we know where a mining pattern exists, we can direct the robot to certain locations where the existence of a mine is very likely. The main idea of coverage based Algorithm is that it cover every point in the search area from star to end [24]. The Whole of the mining area has been divided into small fields. Our algorithm needs a supervisor who can guide the robot along the path to passing the obstacles or monitor the robot.

When a mine is detected, the robot handles destroying mines by time bombs. In the beginning, the robot enters the field in a head-on direction. Until the robot finds any mine, it will move straight forward, searching for mines in front of it. The robot goes in this way until the end of the area, if he did not find any mine in this field, we determine a safe area where we can move our equipment to it. It then turns 180 degrees to cover the adjacent vertical slice and continues this behavior until the whole area has been scanned, as shown in Figure 4. by starting at the circumference and moving in towards the center. The robot scans a single region while remaining stationary and then moves to the next.

Suppose a region R, in indoor environment. R divided into a series of polygonal sub-regions. Using Horizontal decomposition, it is possible to obtain a global undirected graph (Figure 4) G = {S, L} where S and L represent the sub-regions, and connections respectively. The Decomposition Algorithm divides the region into simple, connected sub-regions S; and determines which clear cells are adjacent and constructs a Global Graph.

Fig. 4: Coverage strategy of a minefield [24].


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Each sub-region can be represented as a completed weighted graph S ={V, L, W} where: = {, , }( ≥ 3) is the vertex set = { | , ∈ } is the edge set = { | > 0 ∩ = 0, ∀, ∈ ( )}is the Cost set ℎ is the edge connecting the vertices and is the cost corresponding to edge N (n) is the subset 1, 2... n of the natural number set Testing every possibility for N nodes in a solution would be N! Which is time and resource consuming.

4. Multi-sensor Fusion (Decision Level Fusion) Multisensor system that have different technologies with different false alarms that could decrease

the false alarm rate. Multisensor system also would have the ability to increase the chance of finding different types of mines in different environment conditions. The potential of multisensory systems to improve the detector operating system. The various fusion methods are "decision-level," "feature level" or "data-level" [17, 18].

Our strategy is using a multisensor system that uses different sensors. Our system composite of ultrasound sensor, vapor detector sensor [6], metal detector sensor, camera sensor, and wireless UART sensor. The robot system used decision level technique to deal with all of these sensors. Because of this expert knowledge, we choose decision-level fusion for these applications [7]. We aim to compensate disadvantages of each sensor, so why we use several sensors and decrease false alarms. Metal and plastic mines can be detected using a combined multisensory robot that contains sensors from different techniques.

The proposed multisensor system provides high reliability of mine detection better than using only one best sensor [11]. Every single mine sensor has a false alarms rate for different types of mines, because of a variety of environmental conditions where mines exist [9]. Each method has limitations under certain conditions of the environment and type. Therefore, any technology cannot provide the breakthrough necessary to improve demining operation. Rather than focusing on individual technologies operating in isolation, mine detection robot should have a multisensor system.

The robot used a metal sensor that can detect mines in range 65cm underground. When it goes over a landmine, it sends a signal. We used an ultrasonic sensor that helps in particular environments easily show if a mine should be located nearby, and obstacles avoidance. When ultrasonic detects a landmine, it sends a signal. We used the chemical explosive sensor to allow the detection of most explosives. As the nitrogen levels near explosives are often higher than normal or an easy-to-detect ethanol vapor. The chemical sensor that is located beside the wheels of the robot can detect any lake of mines to increase the probability of mines detection. When the chemical sensor detects a mine, it sends a signal to the planner.

The importance of adding the camera on the car rather than allow the supervisor to follow everything in front of the robot, it also helps in discovering onground mines and discovering the environment. When the camera detects picture of something that looks like the mines samples with images of mines , it sends a signal to the planner. Finally, we collect data from all mounted sensors on a proposed robot. When the collection of signals is two signals or more, this means the probability of mine existence is high, else it will be a false alarm. Therefore, we used calculation to estimate the probablity of nine existence ( ) + ( ) + ( ) + ( )

≥ 2 ( ) If the sum of all probabilities of overall the system is two or more this this is one else false alarm This robot is relatively low-cost in design, and that the development of a suitable scheme for the

robot to do demining and by using more than robot of the same components help us in saving the time.


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Fig. 5: The global control scheme of the robotic vehicle for demining operations.

The best way to remove mines is placing small explosive charges on the identified mine site, moving

away to avoid the blast and detonating the explosives. Other possible ways could be to add a flail to the construction and use this to beat the ground, thus destroying any mines on the surface. A wireless sensor network [22, 23] Using UART wireless tool like ZigBee that can penetrate walls and be used in places where a wired network is difficult to be established. Its main idea is placing the communicating nodes at the required places and sending data to them. It is possible to have good communication rates even at long distances. A wireless sensor is used to overcome all the disadvantages of a wired network. The wireless sensor to find the explosion locations by monitoring the variation in received signal strength from nodes.

Each sensor of the system components provides the operator with a declaration decision from separate signals from each sensor, which are combined to make the overall decision. Our robot has three sensors, each of them produces a separate signal; from the metal detector sensor, the chemical sensor, and ultrasonic sensor. A metal detector is a device that detects metal by a coil that generates an electromagnetic field and measures the eddy currents induced by a metal object. The decision level can be hard which be individual decision (declare or no declare) of component sensors, or can be soft decision rules that give more weight to more reliable decisions. The final decision is based on only the processed signals from the individual sensors. The most important characteristic of decision level fusion the ability to decrease errors and enhance operating characteristics if the multisensor system have different confounders. The decision level is more efficient in demining as the user is entirely responsible for optimizing the system for the field. There is no built-in system intelligence for this task that can adapt easily to the change and difficulty of the environment. As shown in Figure 6, the layout of decision level fusion is presented. If there are two sensors or more send signals, then threshold equals one means there is mine, else it is the safe point.


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Fig. 6: The generic decision-level sensor-fusion layout.

Decision level used a function that defines mines in the field based on the output of the different

sensors. Each sensor has output present the presence of mines that used in measuring confidence level. The main function of these sensors are to generate a weighting factor (wi) that represents the probability of mine existence. Weighting factors are then calculated by fusion function to define whether mine exists or not. When the four sensors send signals from the same point, this means the highest weight and the highest probability of mine existence. When only one sensor sends a signal for mine existence, this point means the lowest weight.

5. Experimental Results

We aimed to decrease false alarms of mines detection. Therefore, we used the multisensor system to

detect mines. As this small robotic unit is limited in functionality and only able to move around detecting mines, a supervisor with a laptop should give orders to the robot. Our robot is small, low-cost, and efficient in mining detection. The proposed robot is a four-wheel car, which mounted three miner sensors. It has wireless sensor to control the robot remotely and also to send data step by step to the supervisor outside the field. This robot is very useful to do operations like detecting mine, obstacle avoidance, and mines defusing.

The main problem with our robot that it takes much time to end the field as the robot must go over every point in the field. The next step is using several small robotic units to sweep an area for mines detection. The advantages of using more than one robot that the time that is needed to search the area decreases. As the area can be divided into smaller, equal parts, where one robotic unit only focuses on the one area. Every robot works separtly in one field and all of the robots onnected to one laptop to monitor them. Our main idea is using more than one small cheap robot that every robot of them mainly responsible for the field of 20 * 20 meter from the whole area. When one robot finds a mine at one place, then this point is defined and this information will be sent to the supervisor across wirless sensor. When more than one sensor from the robot sends signal that there is a mine that means the probability of existence of mine is higher so, the robot sends to the supervisor. The supervisor sends instructions to the robot to dig the ground by the worm robot arm and leaves a ticking time bomb.

The tests were performed indoor as simulation to the landmines environment. Our Technique can be used for determining the actual depth of the subsurface bodies with acceptable accuracy (< 65 cm). The technique proved to be useful in improving the probability of detection of landmines by using the sensor fusion technique better using any of single sensor alone. The results from the evaluation sets are summed and normalized to acquire a detection rate and some false alarms per m^2. As shown in Figure 7, the results of this leave-one-out evaluation proves that the sensor fusion technique performs better than the individual best sensor on the evaluation set.


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Fig. 8: The number of false alarms [m^2].

The whole system has been programmed to work with various kinds of sensors. Our system allows

the operator to stay away at a safe distance and to enable him to control the movement of the robot. The system has been thoroughly tested in several fields with different conditions, and the results proved that this system can increase the probability of detection landmines and reduce hazards to soldiers.

6. Conclusion

In this paper, we have proposed a low-cost multisensor system that can detect mines for reducing

risks to soldiers and guiding them when passing mines area. The proposed system has been configured to work with various kinds of sensors that allow the operator to stay away at a safe distance and to enable him to control the movement of the robot using the IR sensor. The proposed multisensor fusion system used to confirm the operation of the landmine detection and decrease the false rate of detection. We used decision-level fusion because all information from every sensor can be applied as separated. The robot uses a coverage algorithm technique where explicitly passes over all points in the minefield at least once. The algorithm guarantees complete coverage of unknown spaces for ensuring the detected mines existence. The experimental results proved that using multisensor system using decision level fusion when robot move in coverage based motion decreased the false alarm. Our Result proved that using three low-cost sensors is better than using the best sensor alone.

In Future work, we are aiming to use only one expensive metal detector sensor that we will use it to increase distance underground more than 65 cm. In addition, we will use infrared camera images recognition to decrease false alarm and increase the probability of mines detection.

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Landmines Detection Using Low-Cost Multisensory Mobile Robot