Sazalinsyah Razali

Multi-robot cooperation using immune network with memory

In this paper, basic biological immune systems and their responses to external elements to maintain an organisms health state are described. The relationship between immune systems and multi-robot systems are also discussed. Our proposed algorithm is based on immune network theories that have many similarities with the multi-robot systems domain. The paper describes a memory-based immune network that enhance a robots action-selection process and can obtain an overall a quick group response. The algorithm which is named as Immune Network T-cell-regulated — with Memory (INT-M) is applied to the dog and sheep scenario. Simulation experiments were conducted on the Player/Stage platform and experimental results are presented.

An overview of simulation software for non-experts to perform multi-robot experiments

Simulation is an initial approach to determine the experiment feasibility, especially for a complex robotics environment. This paper give an overview of five simulation software for non-expert developers to quickly perform the multirobots simulation. In the advanced robotics field, further research requires simulations, especially which involve multi-robots as to run quickly. There are many robotics simulation software exist. In this paper, we focus only on five simulation software, namely NetLogo, GAMA Platform, Webots, Player/Stage and V-REP. To distinguish the differences between this five tools, we made a comparison of general information and other criteria. Then, we rated them based on the criteria and user necessity. Therefore, the users can easily know which software is suitable for them to use.

Modeling of TiN Coating Thickness Using ANFIS

In this paper, an approach in predicting thickness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The N2 pressure, argon pressure and turntable speed were selected as the input parameters and the coating thickness as an output of the process. Response Surface Methodology (RSM) was used to design the matrix in collecting the experimental data. In the ANFIS structure, three bell shapes were used as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model was validated with confirmatory test data and compared with other prediction models in terms of the root mean square error (RMSE), residual error and prediction accuracy. The result indicated that the developed ANFIS model result was the lowest RMSE7 and average residual error, besides the highest in prediction accuracy compared to the other models. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result.

A conceptual model for multi-robot cooperation inspired by immune network theory and somatic hypermutation

This paper presented biological immune system, immune response, and immune learning through somatic hypermutation. The proposed approach is based on immune network theory since they share common aspects with a multi-robot system. In order to improve the cooperative behavior in multi-robot systems, a conceptual model is presented by integrating the immune network algorithm with somatic hypermutation (SHM) theory. An analogy between the immune system and multi-robot system is discussed as well to show the relationship between immune system environment and robotics environment. The learning mechanism in the antibody is adapted to the robot action thus, the robot is expected to perform better since they adapt to the environment. The proposed model will be implemented in robot simulation environment for future works.

Framework for agent-based buying decision process

In traditional business model, the buying decision process is poorly coordinated among the human decision-makers. Therefore, a long-lived, adaptive, and autonomous application called software agents, that can perform tasks such as personalization, brokering, and negotiation in e-commerce is much needed. These applications reside at the buyers’ side or at the sellers’ servers. The purpose of this paper is to research into possible deployment of software agents in a framework for e-commerce buying decision process. This paper overviews the traditional business model, the Consumer Buying Behavior (CBB) model, and also covers the requirements needed for minimizing human interactions in buying decision processes. The research proposes a software agent’s framework in which, two main approaches, namely Automated Collaborative Filtering (ACF) and Better Business Bureau (BBB), are merged to produce better agents in assisting buying decision process. The framework will enable the agents to get the best price for a good product from a reputable merchant.

Flock identification using connected components labeling for multi-robot shepherding

Shepherding is often used in robotics and applied to various domains such as military in Unmanned Aerial Vehicle (UAV) or Unmanned Ground Vehicle (UGV) combat scenarios, disaster rescue and even in manufacturing. Generally, robot shepherding refers to a task of a robot known as shepherd or sheep herder, who guards and takes care of flocks of sheep, to make sure that the flock is intact and protect them from predators. In order to make an accurate decision, the shepherd needs to identify the flock that needs to be managed. How does the shepherd can precisely identify a group of animals as a flock? How can one actually judge a flock of sheep, is a flock? How does the shepherd decide how to approach or to steer the flock? These are the questions that relates to flock identification. In this paper, a new method using connected components labeling is proposed to cater the problem of flock identification in multi-robot shepherding scenarios. The results shows that it is a feasible approach, and can be used when integrated with the Player/Stage robotics simulation platform.