Highlights
Task:
The task is preparing a review paper for a mini conference.
• In this assessment you are writing a paper “Review paper”, based on two or three published papers that you reviewed, therefore, you need to be very careful about plagiarism. You can download a sample template for your paper using the following link:
• https://www.slac.stanford.edu/econf/editors/eprint-template/instructions.html
• If you use the link, under “Proceeding templates” choose conftemp_one.doc, or any other conference or journal single-column paper template is acceptable.
• After finalising the “Review paper”, you need to prepare your slides for maximum 5 minutes presentation.
• Your review should comprise a simulation part of the main algorithm/model proposed by either one or all papers you reviewed. More simulation, results in improving your mark.
• The references should follow IEEE style. Doctor of Engineering 5 Example: The following papers are some examples:
1. Autonomous navigation of a team of unmanned surface vehicles for intercepting intruders on a region boundary [1]
2. Maximizing the probability of intrusion detection by a fleet of mobile robots using an intelligent game theoretic approach [2] Both papers are on navigation control of multi-mobile robots including the mathematical models, navigation algorithm and decision-making algorithm. However, the main difference is the first one is highly deterministic system however, in the second paper you will find some uncertainty in system which leads to a probabilistic based decision-making process. Both proposed methods are robust and result in a successful navigation of each induvial agent and the entire team consequently, but the methods which have been developed for each are different. In the first paper we can see that the mobile robots’ deployment is the only part which is based on generating random numbers, however in the second one, as the air drop for the initial deployment has been considered, the author(s) used a probability function based on Poisson probability for the robot’s initial deployment. Furthermore, the decision making in the first paper is based on a very certain mathematical method (see Fig.1), however in the second paper, decision making algorithm is developed based on a probability function and game theory which highly depends on pay off function (see Fig.2).
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