Design of Experiments (DOE) is a mathematical methodology employed for information gathering and inference. This research uses the Design of Experiments methodology to analyze the worst case scenario for guiding the UAV (Unmanned Aerial Vehicle) to a maneuverable target by an UAV. Using a minimal set of simulations the DOE provides the worst case target tracking scenario against the results obtained with several simulations. © 2013 IEEE.

Aswani Kumar Cherukuri

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Chithapuram C

Jeppu Y.V.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering

Purpose: The purpose of this paper is to develop a new guidance scheme for aerial vehicles based on artificial intelligence. The new guidance scheme must be able to intercept maneuvering targets with higher probability and precision compared to existing algorithms. Design/methodology/approach: A simulation setup of the aerial vehicle guidance problem is developed. A model-based machine learning technique known as Q-learning is used to develop a new guidance scheme. Several simulation experiments are conducted to train the new guidance scheme. Orthogonal arrays are used to define the training experiments to achieve faster convergence. A well-known guidance scheme known as proportional navigation guidance (PNG) is used as a base model for training. The new guidance scheme is compared for performance against standard guidance schemes like PNG and augmented proportional navigation guidance schemes in presence of sensor noise and computational delays. Findings: A new guidance scheme for aerial vehicles is developed using Q-learning technique. This new guidance scheme has better miss distances and probability of intercept compared to standard guidance schemes. Research limitations/implications: The research uses simulation models to develop the new guidance scheme. The new guidance scheme is also evaluated in the simulation environment. The new guidance scheme performs better than standard existing guidance schemes. Practical implications: The new guidance scheme can be used in various aerial guidance applications to reach a dynamically moving target in three-dimensional space. Originality/value: The research paper proposes a completely new guidance scheme based on Q-learning whose performance is better than standard guidance schemes. © 2016, © Emerald Group Publishing Limited.

International Journal of Intelligent Computing and Cybernetics

Aerial vehicle guidance based on passive machine learning technique

Unmanned Aerial Vehicle(UAV) are used in various applications like visual surveillance of natural resources, product delivery, armed attacks, disaster relief etc. This research proposes use of Artificial Intelligence (AI) for dynamic target tracking capability of an UAV. The AI guidance algorithm performance is compared against standard guidance algorithm like Proportional Navigation Guidance (PNG) algorithm. © 2014 IEEE.

2014 International Conference on Contemporary Computing and Informatics (IC3I)

Artificial Intelligence guidance for Unmanned Aerial Vehicles in three dimensional space

2014 World Congress on Computing and Communication Technologies

A Worst Case Benchmark Problem to Validate Voter Logic

Artificial Intelligence (AI) is a vast domain with variety of applications. This research proposes the use of AI for guidance of an Unmanned Aerial Vehicle (UAV) to a maneuvering target. The AI agent is trained using machine learning algorithm. The PN guidance algorithm is used as a basis for training the agent. An orthogonal array based training strategy is proposed that provides a better training and reduced miss distance values. The AI based guidance provides performance equal to the PN Guidance law and in some cases it outperforms PN guidance law. © 2013 IEEE.

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Artificial Intelligence learning based on proportional navigation guidance

Advanced Tactical and Strategic Missile Guidance, Seventh Edition, Volume 2

Guidance of Unmanned Aerial Vehicles

Aerospace Science and Technology

Designing integrated guidance law for aerodynamic missiles by hybrid multi-objective evolutionary algorithm and tabu search

Control Engineering Practice

Applying a novel extended Kalman filter to missile–target interception with APN guidance law: A benchmark case study

Defence Science Journal

3-D Modified Proportional Navigation Guidance Law based on a Total Demand Vector Concept

Worst case scenario analysis for dynamic target tracking using design of experiments

Journal	Data powered by Typeset2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering
Publisher	Data powered by TypesetIEEE
Open Access	0