KEYWORDS: Fuzzy logic, Unmanned ground vehicles, Unmanned vehicles, Systems modeling, Control systems, Distance measurement, Control systems design, Target detection
Survivability has always been of interest in the defense of any armored vehicles. There has been many reports and papers on the survivability of U.S. Army ground vehicles. A Survivability severity model can be best described as the analogy to the layers of an onion, in which each layer of onion describes a different severity level and the phase of threat detected and severity to apply countermeasures. The objective of this paper is to suggest an evaluation tool that contains an algorithm and procedure for the reliability of manned and unmanned ground vehicles. A decision-making system is proposing for the theoretical survivability and is calculated from a threat level in the form of severity. A generic framework algorithm, consists of both linear and non-linear vehicle dynamics systems, and is included in this paper, which consists of the Fuzzy approach and various scenarios, based on straight path projection. Further, to increase the level of rigor, a layered fuzzy control system using various vehicle dynamics parameters [1] and a methodology for designing an adaptive hierarchical fuzzy model [2] and to accommodate various system parameters dependencies, are describing in this paper as a part of the survivability model. It is hoped that different users will tailor this evaluation tool and used extensively by various research workers working in different area. Several probabilistic cases were included in this paper and implemented by converting to linguistic fuzzy parameters to evaluate the algorithm. A simulation model is designed using supervisory fuzzy rule set and several simulation studies have been done which illustrate the effectiveness of the given approach. The result is a robust d flexible control system.
The necessity and occurrence of Unmanned Aerial Vehicles on the battlefield in near future is increasing day-by-day and that leads to the problem of sharing air space with manned air vehicles. However, avoiding collisions and/or deploying countermeasures during threat detection are a very crucial issue in most of the unmanned aerial vehicles [1] or similar. There is a need for a Sense, Avoid or React [2] system to track objects of potential collision risk or determine any action to avoid or mitigate a collision and, react with countermeasures after detection of hazardous situations i.e., during midair attack, collisions, flight path obstacles or dense clouds. The author of this paper present an algorithm for decision making system based on countermeasures during collision avoidance scenario or threat detection. A general framework to deal with non-linear dynamic systems and will developed consisting of a system of various collision and risks scenarios with moving and stationary threats that is based on straight future projection. The solution will include an algorithm that captures the path prediction of the threat. The proposed optimization problem resolution will aim to maintain minimum separation between two vehicles or threats and applying necessary countermeasures if the collision becomes unavoidable. The multi-sensor fusion system, will generate the object status signal, merges vehicle status, will release an assessment of collision in the form of warning level. A fuzzy controller for countermeasure of friendly maneuver will be presented to generate active and passive measure signals based on the response from assessment signal and vehicle sensor signals. The design implementations and simulations using FPGA will be included.
There has been increasing interest during the last several years in the development of unmanned vehicles. A large
number of such vehicles are soon going to play a major role in defense and security in a battlefield environment. The
objective of the present paper is to ascertain the overall reliability of a large number of unmanned vehicles in the
battlefield. The problem is broken up into two parts, collaboration and coordination of unmanned vehicle network.
Collaboration is the communication between a set of unmanned vehicles which are likely to move in a group.
Coordination is the movement of one group of unmanned vehicle from one source node to another destination node
keeping in view the obstacles and the difficulties in the movement of path. This paper utilizes the existing well known
techniques in the literature for finding the node and terminal reliabilities. These can further be used to obtain the system
reliability of unmanned vehicle network. Fuzzy rules based on experience from past are suggested for the
implementations. A simulation of ground vehicle network having node, branch and terminal simulations is given. It is
hoped that the technique proposed here will prove useful in developing future approaches for ascertaining overall
reliability of unmanned ground networks.
KEYWORDS: Field programmable gate arrays, Sensors, Very large scale integration, Ceramics, Fuzzy logic, Data acquisition, Signal processing, Nondestructive evaluation, Fuzzy systems, System identification
Interest has been shown in the problem of real-time crack detection, crack extent measurement and the identification of
the impact source causing the damage. A solution to the problem of impact source identification is presented using a
signal processing technique employing piezoelectric sensors. In order to detect the crack and to identify the source of the
impact, the Fuzzy logic approach is suggested. Based on the FLA approach, a procedure to develop the rule base is
given. The implementation of the rules is done using Hardware Description Languages (HDL) such as Verilog. The
procedure from Verilog to VLSI implementation is suggested. FPGA implementation and testing of the suggested
procedure is included. The problems for the future work on the development of VLSI to measure the crack and identify
the impact sources are given.
Problem of crack detection has attracted the attention of several investigators in the areas like defense, aeronautics, and
marine industries. In this paper we suggest fuzzy logic approach for detection of cracks and also deciding about the
severity of the crack. The data obtained from data acquisition system is processed and results presented by using various
software. Fuzzy rules are developed to determine the severity of the crack and a light controller used to indicate the
severity of the crack. The simplicity of the approach makes it very useful in many fields.
There is an increasing interest in the army of small unmanned robots taking part in defense operations. It is considered
important to predict the reliability of the group of robots taking part in different operations. A group of robots have both
coordination and collaboration. The robot operations are considered as a network graph whose system reliability can be
determined with the help of different techniques. Once a specified reliability is achieved the commander controlling the
operation can take appropriate action. This paper gives a simulation which can determine the system reliability of the
robotic systems having collaboration and coordination. The procedure developed is based on binary decision diagrams to
obtain a disjoint Boolean expression. The procedure is applicable for any number of nodes and the branches. For
illustration purposes reliability of simple circuits like series network, parallel network, series-parallel and non-series
parallel network are illustrated. It is hoped that more work in this area will lead to the development of algorithms which
will be ultimately used for a real time environment.
Some interest has recently been shown in determining the reliability of unmanned ground vehicles network. Based
on the predicted reliability, a commander can take appropriate action in the battle field operation for unmanned
ground vehicles network. The reliability should include coordination and collaboration of a number of different
unmanned ground vehicles. Some approaches for determining the reliability of unmanned vehicles have been
discussed in the literatures. In this paper, we propose a new algorithm by which the reliability of unmanned ground
vehicles network is predicted using fuzzy approach. The approach is different from statistical approaches discussed
in previous papers. The algorithm suggested here is based on all simple paths obtained between the terminal nodes
of the network in question or the cutset expression of the terminal nodes. Each variable in the Boolean expression
obtained from simple path method or from the cutset method will be subjectively assigned a membership grade by
an expert in the field. Fuzzy union and fuzzy intersection operations are used to predict the reliability of the network
in question. New theorems have been developed to determine the disjoint expression using fuzzy logic. The
comparison of this new approach is given with the existing approaches. Further, assuming node reliability and
branch reliability one can also predict system reliability of the unmanned ground vehicles network using fuzzy logic.
Critical role of unmanned intelligent ground vehicles is evident from variety of defense applications. Fuzzy Reliability
predicts reliability of the convoy of unmanned vehicles represented as a communication network with nodes as vehicle
station and branches as path between the stations. Fuzzy Reliability affirms the performance of the system. Fuzzy
reliability of a convoy of vehicles is the result of Fuzzy and Boolean approaches. The node and branch reliability is
calculated using the Fuzzy approach. The terminal reliability is calculated using Boolean algebra. Software
implementation of the fuzzy reliability is successfully done. To improve the performance evaluation of the convoy, node
failure i.e. failure of convoy station is also taken into consideration. Depending upon the reliability predicted a
commander can take appropriate decision in the battlefield. Proposed algorithm determines all paths from source to
destination and Boolean expressions are formed. A non-overlapping simplification is obtained and further transformed
into mathematical expression, where reliability values are substituted. The results of design, implementation and
simulation of the reliability of convoy of unmanned vehicles are given. It is hoped that the proposed algorithm and its
implementation will be useful for sensor network in general and graph of unmanned vehicles in particular.
With the increasing need of unmanned ground vehicles for combat applications, the collaboration and
coordination of these vehicles have become important design considerations. Both collaboration and
coordination require a large number of sensors. These sensors form a network. The complexity of such
network is very important in the design stage. The objective of this paper is to give a new definition of
complexity which can be used for design and implementation of sensor networks. Algorithms for predicting
the complexity for sensor network are proposed. The implementation of the proposed algorithm is given.
Unmanned intelligent ground vehicles play significant role in wide range of applications. They are of great significance in military applications as well as other commercial applications. In order to assure the performance of unmanned intelligent vehicles, it is important to predict the reliability of the system. Reliability can be calculated using different approaches as seen in the literature, but we propose a Graph theoretic approach supported by Fuzzy and Neuro-Fuzzy approaches for predicting the node and branch reliability of the system. We portray the convoy of unmanned vehicles as a communication network where the nodes represent the station of the convoy of unmanned intelligent vehicles and the branches would represent the path between two stations. The node and branch reliability is calculated using the Fuzzy and Neuro Fuzzy approaches. The terminal and system reliability would be calculated using Boolean algebra. Thus the overall system reliability of a convoy of vehicles is the result of Fuzzy, Neuro-Fuzzy and Boolean approaches. A spanning tree based algorithm is proposed for computation of the system reliability of a convoy of vehicles. We also propose to simulate the overall system reliability with some existing data of factors that contribute in computation of node and branch reliability.
A number of research workers have applied intelligent approaches for robotic applications. In the recent literature there is an increasing role of fuzzy and Neuro fuzzy approaches for unmanned vehicles. Both these approaches are based on intelligent rules. However for these applications the rules become very large and so computational time is very high. It is important to explore the approaches so as to reduce the computation time. In this paper a combination of factor analysis and clustering approaches is suggested so as to reduce the number of rules. The factor analysis can be used to reduce the number of parameters while clustering approach can be used to reduce the number of observations. Based on this methodology a new algorithm is developed which reduces the original parameters and observations into a set of new data. An algorithm is proposed and applied on a real robotic data available in a previous paper. Some of the applications for future work are proposed.
Unmanned ground vehicles have a large number of scientific, military and commercial applications. A
convoy of such vehicles can have collaboration and coordination. For the movement of such a convoy, it is
important to predict the reliability of the system. A number of approaches are available in literature which
describes the techniques for determining the reliability of the system. Graph theoretic approaches are
popular in determining terminal reliability and system reliability. In this paper we propose to exploit Fuzzy
and Neuro-Fuzzy approaches for predicting the node and branch reliability of the system while Boolean
algebra approaches are used to determine terminal reliability and system reliability. Hence a combination of
intelligent approaches like Fuzzy, Neuro-Fuzzy and Boolean approaches is used to predict the overall
system reliability of a convoy of vehicles. The node reliabilities may correspond to the collaboration of
vehicles while branch reliabilities will determine the terminal reliabilities between different nodes. An
algorithm is proposed for determining the system reliabilities of a convoy of vehicles. The simulation of the
overall system is proposed. Such simulation should be helpful to the commander to take an appropriate
action depending on the predicted reliability in different terrain and environmental conditions. It is hoped
that results of this paper will lead to more important techniques to have a reliable convoy of vehicles in a battlefield.
There is an increasing interest in the use of a convoy of unmanned intelligent vehicles for defense and security. These vehicles have a number of sensors associated with them. It is very important to have a highly reliable sensor network so as to determine the dynamic reliability of the intelligent vehicle system for a safe battlefield environment. The mobility, path planning and navigation of such convoy of vehicles are in the state of infancy. However, it is considered important to develop the reliability techniques so that a commander in the battle of field can predict the reliability of the various stages of the movement of the convoy. He can then take decisions depending on reliabilities determined at various places and time. In this paper a combination of intelligent techniques like fuzzy and Boolean algebra techniques are exploited to determine the reliability of the network in the battlefield. The branches of reliabilities are determined using intelligent approaches like fuzzy logic while terminal reliabilities are determined using Boolean algebra. Based on this approach, a new algorithm is proposed in determining the dynamic reliability of convoy of unmanned intelligent vehicles. Such an approach will help in the collaboration and coordination of the convoy of vehicles.
There has been an increasing interest of unmanned vehicles keeping the importance of defense and security. A few
models for a convoy of unmanned vehicle exist in literature. The objective of this paper is to exploit agent based
modeling technique for a convoy of unmanned vehicles where each vehicle is an agent. Using this approach, the convoy
of vehicles reaches a specified goal from a starting point. Each agent is associated with number of sensors. The agents
make intelligent decisions based on sensor inputs and at the same time maintaining their group capability and behavior.
The simulation is done for a battlefield environment from a single starting point to a single goal. This approach can be
extended for multiple starting points to reach multiple goals. The simulation gives the time taken by the convoy to reach
a goal from its initial position. In the battlefield environment, commanders make various tactical decisions depending
upon the location of an enemy outpost, minefields, number of soldiers in platoons, and barriers. The simulation can help
the commander to make effective decisions depending on battlefield, convoy and obstacles to reach a particular goal.
The paper describes the proposed approach and gives the simulation results. The paper also gives problems for future
research in this area.
Development of software for autonomous ground vehicles has been a mission critical issue for army. For the last two decades, different definitions of software complexity have been proposed. However, no definition has been found to be highly satisfactory. So in this report, a new definition of software complexity based on the rank of a matrix of a finite sequential machine has been proposed. Software has been developed to determine the complexity, so that it could be used in the early stages of software development from the data flow architecture to save cost and development efforts. We have shown the examples of data flow architecture and then complexity calculation based on our software. It is hoped that the results in this paper will help software development to address reliability and complexity issues of autonomous ground vehicle in a better way to assist research in defense and security for such missions.
There is an increasing interest in developing new Mobile Robots because of their applications in a variety of areas. Mobile robots can reach places, which are either inaccessible or unsafe for human beings. TACOM has developed a lab where new mobile robots can be tested. However to save cost and time it is advisable to test robots in a virtual environment before they are tested in a real Lab. The objective of this paper is to explore techniques whereby mobile robots can be tested in a simulated environment. Different techniques have been studied for such simulations and testing in a virtual environment. In particular, State flow and Zed3d software, VRML and Fuzzy Logic approaches have been exploited for this purpose. Different robots, obstacles and terrains have been simulated. It is hoped that such work will prove useful in the study of development and testing of mobile robots.
The use of near, mid wavelength and long wavelength infrared imagery for the detection of mines and concealed weapons is demonstrated using several techniques. The fusion algorithms used are wavelet based fusion and Fuzzy Logic Approach (FLA) fusion. The FLA is presented as one of several possible methods for combining images from different sensors for achieving an image that displays more information than either image separately. Metrics are suggested that could rate the fidelity of the fused images, such as, an entropy metric.
Thomas Meitzler, David Bednarz, Eui Sohn, Kimberly Lane, Darryl Bryk, Elena Bankowski, Gulsheen Kaur, Harpreet Singh, Samuel Ebenstein, Gregory Smith, Yelena Rodin, James Rankin
The fusion of visual and infrared sensor images of potential driving hazards in static infrared and visual scenes is computed using the Fuzzy Logic Approach (FLA). The FLA is presented as a new method for combining images from different sensors for achieving an image that displays more information than either image separately. Fuzzy logic is a modeling approach that encodes expert knowledge directly and easily using rules. With the help of membership functions designed for the data set under study, the FLA can model and interpolate to enhance the contrast of the imagery. The Mamdani model is used to combine the images. The fused sensor images are compared to metrics to measure the increased perception of a driving hazard in the sensor-fused image. The metrics are correlated to experimental ranking of the image quality. A data set containing IR and visual images of driving hazards under different types of atmospheric contrast conditions is fused using the Fuzzy Logic Approach (FLA). A holographic matched-filter method (HMFM) is used to scan some of the more difficult images for automated detection. The image rankings are obtained by presenting imagery in the TARDEC Visual Perception Lab (VPL) to subjects. Probability of detection of a driving hazard is computed using data obtained in observer tests. The matched-filter is implemented for driving hazard recognition with a spatial filter designed to emulate holographic methods. One of the possible automatic target recognition devices implements digital/optical cross-correlator that would process sensor-fused images of targets. Such a device may be useful for enhanced automotive vision or military signature recognition of camouflaged vehicles. A textured clutter metric is compared to experimental rankings.
KEYWORDS: Fuzzy logic, Data modeling, Visualization, Wavelets, Systems modeling, Visual process modeling, Wavelet transforms, Target detection, Target acquisition, Fuzzy systems
The mean search time of observers looking for targets in visual scenes with clutter is computed using the Fuzzy Logic Approach (FLA). The FLA is presented by the authors as a robust method for the computation of search times and or probabilities of detection for signature management decisions. The Mamdani/Assilian and Sugeno models have been investigated and are compared. A 44 image data set from TNO is used to build and validate the fuzzy logic model for detection. The input parameters are the: local luminance, range, aspect, width, wavelet edge points and the single output is search time. The Mamdani/Assilian model gave predicted mean search times from data not used in the training set that had a 0.957 correlation to the field search times. The data set is reduced using a clustering method then modeled using the FLA and results are compared to experiment.
The probability of detection (Pd) of targets in infrared and visually cluttered scenes is computed using the Fuzzy Logic Approach (FLA). The FLA is presented by the authors as a robust and high fidelity method for the computation and prediction of the Pd of targets. The Mamdani/Assilian, Sugeno and Neurofuzzy-based models have been investigated. A limited data set of visual imagery has been used to model the relationships between several input parameters; the contrast, camouflage condition, range, aspect, width, and experimental Pd. The fuzzy and neuro-fuzzy models gave predicted Pd values that had 0.98 correlation to the experimental Pd's. The results obtained indicate the robustness of the fuzzy-based modeling techniques and the applicability of the FLA to those types of problems having to do with the modeling of human object detection and perception in any spectral regime.
Wavelet transforms are currently being used for a number of applications such as cue feature and noise extraction form images and acoustic signals. The objective of this paper is to describe and apply the author's algorithm that uses wavelets for finding the clutter in infrared and visual images. Once the clutter is found, the probability of detection is calculated. The Reynolds identity and Tidhar's and Rotman's probability of edge metric are extended to encompass the wavelet methodology for multiscale clutter metrics in IR and visual images.
The determination of number of neurons (H) in hidden layers is very important as it affects the training time and generalization property of neural networks. A higher value of H may force the network to memorize (as opposed to generalize) the patterns which it has seen during training whereas a lower value of H would waste a great deal of training time in finding its optimal representation. It is thus important to devise some methods by which a proper selection of neurons in hidden layers can be made. In this paper, a procedure has been given which determines the number of separable regions (M) in binary error correcting codes (BECC). Thus it is possible to establish link between input training patterns (T), M, and H for such codes without running simulations. Theorems have been developed which provide justification of the use of implied minterm structure (IMS) to BECC. It is shown that BECC are nonlinearly separable (LS) and canonical. Investigations have also been conducted on systematic and nonsystematic codes to prove that systematic codes can be classified with a lesser value of H than the nonsystematic codes as systematic codes require less number of separable regions for their realization.
KEYWORDS: Optical discs, Data storage, Magnetism, Process modeling, Optical storage, Data modeling, Performance modeling, Systems modeling, Optics manufacturing, Neodymium
A new approach to modeling and evaluating storage media for archival purposes is presented. Different models are presented with varying document storage requirements ranging from 25,000 to 1 million per day. This evaluation process can be very useful for a variety of industrial applications where very large amounts of storage over the years is required. A media selection methodology is given to assist users in the evaluating process. The optical disk is a stable, reliable, high-capacity, high-performance, and very cost-effective storage media for data storage and archival applications. The disk provides very high data storage capacity for a low cost per megabyte compared to other on-line storage devices. Optical disk-based storage technology is recommended for archival storage for various applications in the data and image processing area, such as engineering, manufacturing, document management, banking, insurance, and health care. We evaluate different storage media for long-term archival of large amounts of data and the cost-effectiveness of different storage media for various models based on document storage requirements.
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