Adam Mustapha

Modeling and simulation of reliability of unmanned intelligent vehicles

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.

On the reliability of a convoy of unmanned intelligent vehicles and their collaboration and coordination

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.

A new approach for determining reliability of unmanned vehicles network using fuzzy logic

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.

On Software Implementation of Reliability of Unmanned Ground Vehicles

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.

Complexity of Robotic Sensor Networks

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.

Fuzzy System Reliability Computation of the Convoy of Unmanned Intelligent Vehicles

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.

On a new approach to reduction of data for ANFIS application to unmanned robotic vehicles

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.