Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools

Object Detection in X-ray Images Using Transfer Learning with Data Augmentation

Object detection in X-ray images is an interesting problem in the field of machine vision. The reason is that images from an X-ray machine are usually obstructed with other objects and to itself, therefore object classification and localization is a challenging task. Furthermore, obtaining X-ray data is difficult due to an insufficient dataset available compared with photographic images from a digital camera. It is vital to easily detect objects in an X-ray image because it can be used as decision support in the detection of threat items such as improvised explosive devices (IED’s) in airports, train stations, and public places. Detection of IED components accurately requires an expert and can be achieved through extensive training. Also, manual inspection is tedious, and the probability of missed detection increases due to several pieces of baggage are scanned in a short period of time. As a solution, this paper used different object detection techniques (Faster R-CNN, SSD, R-FCN) and feature extractors (ResNet, MobileNet, Inception, Inception-ResNet) based on convolutional neural networks (CNN) in a novel IEDXray dataset in the detection of IED components. The IEDXray dataset is an X-ray image of IED replicas without the explosive material. Transfer learning with data augmentation was performed due to limited X-ray data available to train the whole network from scratch. Evaluation results showed that individual detection achieved 99.08% average precision (AP) in mortar detection and 77.29% mAP in three IED components

Automation and Control for Adaptive Management System of Urban Agriculture Using Computational Intelligence

It has been predicted by the United Nations that the world population will increase to 9.8 billion in 2050. This causes agricultural development areas to be transformed into urban areas. This urbanization and increase in population density cause food insecurity. Urban agriculture using precision farming becomes a feasible solution to meet the growing demand for food and space. An adaptive management system (AMS) is necessary for such farm to provide an artificial environment suitable to produce cultivars effectively. This research proposes the development of a computational intelligence-based urban farm automation and control system utilizing machine learning and fuzzy logic system models. A quality assessment is employed for adjusting the environmental parameters with respect to the cultivars’ requirements. The system is composed of sensors for data acquisition and actuators for model-dictated responses to stimuli. Data logging was done wirelessly through a router that would collect and monitor data through a cloud-based dashboard. The model intended for training from the acquired data undergo statistical comparative analysis and least computational cost analysis to optimize the performance. The system performance was evaluated by monitoring the conditions of the sensors and actuators. Experiment results showed that the proposed system is accurate, robust, and reliable

Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

This study proposes the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to correct the latitude and longitude of Global Positioning System (GPS) used in locating towed vehicle system for underground imaging. The input used was the collected data from a developed Real-time Kinematic Global Positioning System sensor integrated with Inertial Measurement Unit. Different ANFIS models were developed and evaluated. For latitude correction, ANFIS model with hybrid optimization trained at 300 epochs was chosen, whereas for longitude correction, ANFIS model with hybrid optimization trained at 100 epochs was selected. Both models achieved the lowest Mean Squared Error (MSE), the highest Coefficient of Determination (R2), and lowest Mean Absolute Error (MAE). Moreover, selected best ANFIS models were compared to Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) models, but the results showed that the ANFIS models have superior performances. The selected ANFIS models were verified by testing on the collected actual dataset and the visualized map demonstrated that the corrected GPS latitude and longitude have significantly reduced error, indicating that the fuzzy system with neural network capabilities is a cost-effective and convenient method for error reduction in vehicle localization making it applicable to be integrated for capacitive resistivity underground imaging systems

Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

Swarm intelligence development environment and underwater swarm robotics platform

The main objective of this research is to show that swarm intelligence and underwater swarm robotics is a possible technology for underwater wireless data communication, which up to present time is very challenging. Underwater wireless communication is similar to terrestrial communication having the worst parameters. This research paper is divided into two major components. First is the design, development, testing and validation of a Swarm Intelligence Development Environment and 3 different swarm intelligence for data communications. Second is the design, development testing and validation of an underwater swarm robotics platform. These two major components are necessary in order to successfully meet the objective. The three swarm intelligence designed are slime mold, trophallaxis and pheromone based swarm intelligence. Slime mold is based on the social amoeba called Dictyostelium discoideum. Trophallaxis is based on the behavior of bees. Pheromone is based on the behavior of ants. The three swarm intelligence are designed, developed and tested using the Swarm Intelligence Development Environment coded by the author. The underwater warm robotics platform is composed of six robots. All robots are design by the author using mechanical and electronics engineering principles. Codes are also designed by the author. The swarm behaviors are embedded in the system. Testing and validation are performed in a swimming pool. The testing and validation results support the objective of the researcher. The simulation results show the performance of the three swarm behaviors. It can be seen that their performance parameters are affected by which behavior is used and how many robots are used. The underwater swarm robotics platform is able to show swarm behavior and supports the overall success of the research

Fuzzy logic motor speed controller for soccer playing robots

This study is focused in the design, development and benchmarking of a fuzzy logic motor speed controller for soccer playing robots. It is coded in Clanguage for an Atmel ATmega128 Microcontroller. The inputs for the fuzzy logic are the left velocity and right velocity that is sent by the host computer. While they are referred to as velocities, these values are simple a range of numbers representing stop, the slowest up to the fastest velocity for both forward and reverse. The fuzzy logic motor speed controller converts these values to the actual velocity to be performed by the motors. Also, the fuzzy logic motor speed controller made it possible to compensate the errors produced by motor differences. A series of drills and experiments were done to benchmark the Fuzzy Logic Motor Speed Controller versus the original non-fuzzy controller of the Neuronemech soccer robots. As shown in the test results and actual performance of the robots, the fuzzy logic motor speed controller is found to be effective in characterizing the speed assignment and resolution of a specific motor and performed better than the original non-fuzzy logic controller

IEEE CIS Philippines Chapter report

The IEEE Computational Intelligence Society (CIS) Philippines Chapter organized the first part of the IEEE CIS Philippines Chapter Distinguished Lecture Series on November 4, 2011, Manila. During the event, various members of the IEEE CIS Philippines Chapter presented their research work. Engr. Ryan Rhay Vicerra presented \u27Robot path planning simulator using genetic algorithm\u27, Engr. Alejandro Ballado presented \u276-element high-gain Yagi-Uda antenna design for RFID tag using genetic algorithm\u27, and Engr. Erwin Daculan, \u27Optimization of test functions using simple genetic algorithm\u27. The second part of the lecture series was held on December 14, 2011, that included lecture given by Dr. Gary Yen of Oklahoma State University on \u27Cultural- based particle swarm optimization for multi-objective optimization and performance metrics ensemble\u27. Dr. Gary Yen also introduced IEEE CIS, its history, mission and activities during the seminar

Slime mold inspired swarm robot system for underwater wireless data communication

Swarm robotics is a collection of mobile robots that displays swarm behavior. This paper presents a simulator of slime mold amoeba inspired swarm robot for underwater wireless communication system. The slimemold inspired robotic swarm is used to overcome the challenges of transmitting data in a large underwater environment. Underwater communication systems today are primarily acoustic technology and characterized by limited and distance dependent bandwidth, presence of multipath, and low speed of sound propagation. The robots navigate and seek the shortest path creating a virtual connection between the data transmitter and receiver similar to the foraging behavior of swarms. Each individual robot going back and forth from the transmitter to the receiver and vice-versa acts as a physical carrier of the data. Swarm robots navigate using swarmlevel intelligence based on the signal propagation technique used by slime mold amoeba aggregation using acoustics communication. The robot swarm system is developed, simulated and tested using the coded simulator. Using the slime mold inspired swarm robot system; the simulation successfully performed the data foraging scenario and showed the ability of the swarm to provide a virtual link in an underwater wireless communication network

Use of artificial neural network in the estimation of detonation velocity for tetranitromethane-nitrobenzene mixture

Detonation velocity or rate of energy release is an important property to consider when rating an explosive. It is a critical parameter used for estimating explosive performance as it can indicate the intensity of detonation. The purpose of this research study is to propose an artificial neural network model that would aid in the estimation of detonation velocities of a high explosive specifically, tetranitromethane-nitrobenzene (TNM/NB) mixture, with varying parameters. © 2019 IEEE