Penghasilan dan penilaian buku panduan prosedur pematerian komponen elektronik

Projek "Penghasilan dan Penilaian Buku Panduan Prosedur Pematerian Komponen Elektronik" ini adalah projek yang bertujuan untuk melihat penerimaan pelajar terhadap penghasilan buku panduan ini. Kajian juga dijalankan untuk melihat kepentingan buku panduan dalam membantu proses pembelajaran di makmal. Seramai 60 orang pelajar Tahun 2, Semester 2 Diploma Teknologi Kejuruteraan Elektrik, KUiTTHO telah dijadikan sampel kajian. Borang soal selidik dijadikan sebagai instrumen kajian seterusnya dianalisis menggunakan perisian Statistical Package For Sosial Science 11.0 for Windows (SPSS). Dapatan menunjukkan penggunaan buku panduan penting dalam proses pembelajaran di makmal. Buku Panduan Penggunaan Prosedur Pematerian Komponen Elektronik yang telah dihasilkan, jelas membuktikan bahawa aspek isi kandungan, rekabentuk persembahan dan kebolehgunaannya mempengaruhi penerimaan pelajar terhadapnya. Buku panduan yang diperkenalkan ini dapat membantu memudahkan proses pembelajaran para pelajar di makmal

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

A realistic evaluation of indoor robot position tracking systems: The IPIN 2016 competition experience

We report a novel open competition aimed at evaluating accurate robot position tracking in indoor environments. The competition was organized within the IPIN 2016 (Indoor Positioning and Indoor Navigation international Conference). Here, we describe the competition, the competitors and their final results. The challenges of this new competition included: tracking an industrial robot following an unknown path but with a defined ground-truth, and open positioning system to be deployed on-site, with no restrictions apart from those related to safety issues. Our aim here is to provide sufficient detail to serve as a solid basis for future competition initiatives with a similar scope, using common metrics and objective evaluation procedures. In addition, the real systems evaluated represent state-of-the-art performance, and thus offer interesting solutions to the problem posed in the competition.Agencia Estatal de InvestigaciónUniversidad de Alcal

Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Vision-Based Monocular SLAM in Micro Aerial Vehicle

Micro Aerial Vehicles (MAVs) are popular for their efficiency, agility, and lightweights. They can navigate in dynamic environments that cannot be accessed by humans or traditional aircraft. These MAVs rely on GPS and it will be difficult for GPS-denied areas where it is obstructed by buildings and other obstacles.  Simultaneous Localization and Mapping (SLAM) in an unknown environment can solve the aforementioned problems faced by flying robots.  A rotation and scale invariant visual-based solution, oriented fast and rotated brief (ORB-SLAM) is one of the best solutions for localization and mapping using monocular vision.  In this paper, an ORB-SLAM3 has been used to carry out the research on localizing micro-aerial vehicle Tello and mapping an unknown environment.  The effectiveness of ORB-SLAM3 was tested in a variety of indoor environments.   An integrated adaptive controller was used for an autonomous flight that used the 3D map, produced by ORB-SLAM3 and our proposed novel technique for robust initialization of the SLAM system during flight.  The results show that ORB-SLAM3 can provide accurate localization and mapping for flying robots, even in challenging scenarios with fast motion, large camera movements, and dynamic environments.  Furthermore, our results show that the proposed system is capable of navigating and mapping challenging indoor situations

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ