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Abstract

Production and assessment of artillery firing tables (FT) are the key tasks in solving ballistic problems through both standard and non-standard firing conditions. According to the literature, two different standard firing table formats were developed by the former-Soviet and the United States armies. This study proposes the main difference between these FT formats, as the standard meteorological conditions. An accuracy assessment has been proposed to justify different sources of errors through modeling and production of such tables, including applied meteorological message, aiming angles round-off, linear superposition principle, and Earth approximation. A~case study has been proposed for the 155M107 projectile to demonstrate the impact of the Coriolis effect as well as other ballistic and atmospheric non-standard conditions. As a part of the construction of artillery FT, a fitting process has to be made between available firing data and simulations. Therefore, a parametric study is implemented to study the number of test elevations per charge needed through the fitting process and its corresponding production error. Hence, based on the number of test elevations available, the genetic algorithm (GA) has been utilized to obtain the test elevations order needed with minimum FT production error. The results show a good agreement with the data stated in the literature.
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Authors and Affiliations

Mostafa Khalil
1
ORCID: ORCID

  1. Aerospace Engineering Department, Military Technical College, Cairo, Egypt
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Abstract

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.

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Authors and Affiliations

Madhusmita Panda
Bikramaditya Das
Bibhuti Bhusan Pati

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