Inertial Measurement Units (IMUs) in Mobile Robots over the Last Five Years: A Review

Robots and especially mobile robots have experienced rapid growth, making them part of everyday life. An inertial measurement unit (IMU), which is a set of sensors, plays an important role in mobile robots’ navigation. Data collected by the IMU sensors on a robot are properly converted and useful information is calculated concerning, i.e., position, orientation, and acceleration. With the advancement of technology, IMUs have been transformed from large and complex devices into small, flexible, and efficient ones. The main sensors included in an IMU are the gyroscope, the accelerometer, and the magnetometer. Additionally, there are other sensors such as a barometer, a temperature sensor, a pressure sensor, or even an attitude sensor. The components that an IMU consists of are many and the main differences concern the technology they integrate, the designer purpose, and the specifications set by the manufacturer. The purpose of this review is a comparative presentation of 42 IMU models from 7 different manufacturers over the last five years comparing main features such as structure details, connectivity, and communication protocols. Moreover, statistical results are quantitatively and qualitatively presented providing a future user the possibility to select the proper IMU

Evolution towards Hybrid Software Development Methods and Information Systems Audit Challenges

The key objective of this paper is to investigate the evolution of hybrid software development methods and highlight the main difficulties that arise with regard to information systems (IS) auditing. While technology firms today are under constant pressure to deliver software faster due to emerging needs worldwide, this continuous effort leads to innovative development models, apparently driven by practice. Since modern software development is neither pure linear phases progression nor agile, a challenge arises with regards to selecting the appropriate combination of approaches that serve to reach goals and assure value creation for organizations

EURASIP Journal on Applied Signal Processing 2005:14, 2268–2280 c ○ 2005 Theodore P. Pachidis et al. Robot Path Generation Method for a Welding System Based on Pseudo Stereo Visual Servo Control

A path generation method for robot-based welding systems is proposed. The method that is a modification of the method “teaching by showing ” is supported by the recently developed pseudo stereovision system (PSVS). A path is generated by means of the target-object (TOB), PSVS, and the pseudo stereo visual servo control scheme proposed. A part of the new software application, called humanPT, permits the communication of a user with the robotic system. Here, PSVS, the robotic system, the TOB, the estimation of robot poses by means of the TOB, and the control and recording algorithm are described. Some new concepts concerning segmentation and point correspondence are applied as a complex image is processed. A method for calibrating the endpoint of TOB is also explained. Experimental results demonstrate the effectiveness of the proposed system

Grapevine Plant Image Dataset for Pruning

Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow and produce grapes. It is a difficult procedure, and it is not yet fully automated. However, some attempts have been made by the research community. Based on the literature, grapevine pruning automation is approximated with the help of computer vision and image processing methods. Despite the attempts that have been made to automate grapevine pruning, the task remains hard for the abovementioned domains. The reason for this is that several challenges such as cane overlapping or complex backgrounds appear. Additionally, there is no public image dataset for this problem which makes it difficult for the research community to approach it. Motivated by the above facts, an image dataset is proposed for grapevine canes’ segmentation for a pruning task. An experimental analysis is also conducted in the proposed dataset, achieving a 67% IoU and 78% F1 score in grapevine cane semantic segmentation with the U-net model

Grapevine Plant Image Dataset for Pruning

Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow and produce grapes. It is a difficult procedure, and it is not yet fully automated. However, some attempts have been made by the research community. Based on the literature, grapevine pruning automation is approximated with the help of computer vision and image processing methods. Despite the attempts that have been made to automate grapevine pruning, the task remains hard for the abovementioned domains. The reason for this is that several challenges such as cane overlapping or complex backgrounds appear. Additionally, there is no public image dataset for this problem which makes it difficult for the research community to approach it. Motivated by the above facts, an image dataset is proposed for grapevine canes’ segmentation for a pruning task. An experimental analysis is also conducted in the proposed dataset, achieving a 67% IoU and 78% F1 score in grapevine cane semantic segmentation with the U-net model

Machine Vision for Ripeness Estimation in Viticulture Automation

Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined

Toward Big Data Manipulation for Grape Harvest Time Prediction by Intervals' Numbers Techniques

The automation of agricultural production calls for accurate prediction of the harvest time. Our interest in particular here is in grape harvest time. Nevertheless, the latter prediction is not trivial also due to the scale of data involved. We propose a novel neural network architecture that processes whole histograms induced from digital images. A histogram is represented by an Intervals' Number (IN); hence, all-order data statistics are represented. In conclusion, the proposed IN Neural Network, or INNN for short, emerges with the capacity of predicting an IN from past INs. We demonstrate a proof-of-concept, preliminary application on a time series of digital images of grapes taken during their growth to maturity. Compared to a conventional Back Propagation Neural Network (BPNN), the results by INNN are superior not only in terms of prediction accuracy but also because the BPNN predicts only first-order data statistics, whereas the INNN predicts all-order data statistics