Petrus Mursanto

Simulation of intelligent Unmanned Aerial Vehicle (UAV) For military surveillance

Nowadays, Unmanned Aerial Vehicle (UAV) is an important technology for military and security application. Various missions can be done using UAV such as surveillance in unknown areas, forestry conservation, and spying enemy territory. Application which is developed in this research has a purpose to simulate condition in war zone for spying the enemy. Platform used in the experiment is Parrot AR. Drone ver.2.0, an mini quadrotor which was developed by Parrot SA. This quadrotor controlled by Robot Operating System (ROS) framework. The quadrotor will search and recognize some objects and locate their location. Many algorithms were used to do the mission. To recognize object Adaboost Classifier and Pinhole Algorithm were used. The result shows that average error for all scenario is only 0.24 meters.

Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD)

Information extraction using distributed sensors has been widely used to obtain information knowledge from various regions or areas. Vehicle traffic data extraction is one of the ways to gather information in order to get the traffic condition information. This research intends to predict and visualize the traffic conditions in a particular road region. Traffic data was obtained from Department of Transport UK. These data are collected using hundreds of sensors for 24?h. Thus, the size of data is very huge. In order to get the behavior of the traffic condition, we need to analyze the huge dataset which was obtained from the sensors. The uses of conventional data mining methods are not sufficient to use, due to the process of knowledge building that should store data temporary in the memory. The fact that data is continuously becoming larger over time, therefore we need to find a method that could automatically adapt to process data in the form of streams. We use method called FIMT-DD (Fast Incremental Model Trees-Drift Detection) to analyze and predict the very large traffic dataset. Based on the prediction system that we have developed, we also visualize the prediction of traffic flow condition within generated sensor point in the real map simulation.

Vehicle counting and speed measurement using headlight detection

CCTV is one of the tools that can be used to extract the needed traffic Information. Extracted information from image sequences of CCTV can give us real information about the number of passing vehicles and vehicles speed. In this paper we propose a new method in detecting the number of vehicles and vehicle speed measurement in low light conditions. Headlight detection is used in order to identify the existing vehicle. There are few steps in order to extract the information from CCTV. First for vehicle headlight detection, the vehicles are detected with normalized cross-correlation method and centroid-area-difference. The second step is vehicle tracking. Headlight is used to track the movements of the vehicle. The third step is vehicle counting and vehicle speed measurement; pin-hole and euclidean distance methods are used to estimate the vehicle speed. We have compared the vehicle detection algorithm and vehicle counting-speed measurement. The result shows that the normalized cross correlation method has a higher accuracy than area-centroid difference. The pinhole model also is better in estimating vehicle speed compared to euclidean distance.

Adaptive traffic signal control system using camera sensor and embedded system

Adaptive traffic signal control system is needed to avoid traffic congestion that has many disadvantages. This paper presents an adaptive traffic signal control system using camera as an input sensor that providing real-time traffic data. Principal Component Analysis (PCA) is used to analyze and to classify object on video frame for detecting vehicles. Distributed Constraint Satisfaction Problem (DCSP) method determine the duration of each traffic signal, based on counted number of vehicles at each lane. The system is implemented in embedded systems using BeagleBoard™.

Autonomous quadcopter swarm robots for object localization and tracking

A swarm Unmanned Aerial Vehicle (UAV) or quad copter robot for object localization and tracking has been developed. The robot is potentially utilized for military purpose, i.e. doing patrol continuously especially in frontier area. In other words, the UAV is proposed to carry out patrol and exploration by exploring coverage area, find, localize and track suspicious objects. The swarm robots are equipped with Modified Particle Swarm Optimization (PSO) Algorithm for intelligent feature. PSO is an optimization algorithm where each agent of swarm will use its individual perception (local base) and community perception (global base). This swarm quad copter system was implemented using Robot Operating System (ROS) Framework. Experiment was conducted with 3 quadcopter agents and one object as the target. Two main scenarios have been exercised, i.e. a scenario with steady target and another one with moving target. Experimental result shows that Modified PSO implemented in this system has better performance compared to fully random based moving algorithm for object localization and tracking.

Enhanced adaptive traffic signal control system using camera sensor and embedded system

Traffic plays an important role in social stability and community development. Without an appropriate traffic signal control system, the possibility of traffic congestion will be very high and causes various negative impacts. The traffic signal control system with video camera sensor is implemented in embedded systems using BeagleBoard-xM. The system uses Viola-Jones method and Haar Training in detecting the vehicle object from a video frame. Then, Euclidean distance and kalman filter methods are used in tracking the vehicle. The ability of kalman filter in predicting the next position of the object is a very important feature for multi-object tracking. The number of counted vehicles in each lane at the intersection then will be processed using Fuzzy Logic to determine optimal cycle time and split time.

Telecommunication networks coverage area expansion in disaster area using autonomous mobile robots: Hardware and software implementation

Communication network expansion technology using autonomous robot covers three research fields which includes network expansion, network routing, and localization. On this research, we implement an efficient robot formation and network expansion algorithm on a simulator based on Open Dynamics Engine (ODE) library. We extended the robot formation algorithm to handle multiple sources implementation to keep communication linkage between communication towers. We also developed a network routing algorithm and implement the robot formation algorithm on real autonomous robots hardware. The result of simulation shows that our proposed communication network expansion technology is visible to be implemented. Therefore, we designed an autonomous robot meeting the requirement set in the simulation.

Background Subtraction Using Gaussian Mixture Model Enhanced by Hole Filling Algorithm (GMMHF)

There is a necessity in traffic control system using camera to have the capability to discriminate between an object and non-object in the image. One of the procedure to discriminate between those two is usually performed by background subtraction. Gaussian Mixture Model (GMM) is popular method that has been employed to tackle the problem of background subtraction. However, the output of GMM is a rather noisy image which comes from false classification. This situation may arise because several conditions in the video input such as, waving trees, rippling water, and illumination changes. In this paper, an enhanced version of GMM technique which is combined with Hole Filling Algorithm (HF) is proposed to alleviate those problems. The experimental result shows that the proposed method improved the accuracy up to 97.9% and Kappa statistic up to 0.74. This result has outperformed many similar methods that is used for evaluation.

Localizing multiple odor sources in dynamic environment using ranged subgroup PSO with flow of wind based on open dynamic engine library

A new algorithm based on Modified Particle Swarm Optimization (MPSO) which follows a local gradient of the chemical concentration within a plume and follow direction of the wind velocity is investigated. Moreover, the niche or parallel search characteristic is adopted on MPSO to solve the multi-peak and multi-source problem. When using parallel MPSO, subgroup of robot is introduced then each subgroup can locate the odor source. Unfortunately, there is a possibility that more that one subgroup locates one odor sources. This is inefficient because other subgroups locate other source, then we proposed a ranged subgroup method for coping for that problem, then the searching performance will increase. Then ODE (Open Dynamics Engine) library is used for physical modeling of the robot like friction, balancing moment and others. Finally the statistical analysis shows that the new approach is technically sounds.

Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm

Vehicle speed estimation using Closed Circuit Television (CCTV) is one of the interesting issues in the field of computer vision. Various approaches are used to perform automation in vehicle speed estimation using CCTV. In this study, the use of Gaussian Mixture Model (GMM) for vehicle detection has been improved with the hole-filling method (HF). The speed estimation of the vehicles with various scenarios has been done, and gives the best estimation with the deviation of 7.63 Km/hr. GMM fusion with hole-filling algorithm combined with Pinhole models have shown the best results compared with results using other scenarios.