Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment

Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

With the rise of transportation electrification, autonomous driving and shared mobility in urban mobility systems, and increasing penetrations of distributed energy resources and autonomous demand-side management techniques in energy systems, tremendous opportunities, as well as challenges, are emerging in the forging of a sustainable and converged urban mobility and energy future. This paper is motivated by these disruptive transformations and gives an overview of managing autonomous mobility and energy services in future smart cities. First, we propose a three-layer architecture for the convergence of future mobility and energy systems. For each layer, we give a brief overview of the disruptive transformations that directly contribute to the rise of autonomous mobility-on-demand (AMoD) systems. Second, we propose the concept of autonomous flexibility-on-demand (AFoD), as an energy service platform built directly on existing infrastructures of AMoD systems. In the vision of AFoD, autonomous electric vehicles provide charging flexibilities as a service on demand in energy systems. Third, we analyze and compare AMoD and AFoD, and we identify four key decisions that, if appropriately coordinated, will create a synergy between AMoD and AFoD. Finally, we discuss key challenges towards the success of AMoD and AFoD in future smart cities and present some key research directions regarding the system-wide coordination between AMoD and AFoD.Comment: 19 pages, 4 figure

Hypersonic Research Vehicle (HRV) real-time flight test support feasibility and requirements study. Part 2: Remote computation support for flight systems functions

The requirements are assessed for the use of remote computation to support HRV flight testing. First, remote computational requirements were developed to support functions that will eventually be performed onboard operational vehicles of this type. These functions which either cannot be performed onboard in the time frame of initial HRV flight test programs because the technology of airborne computers will not be sufficiently advanced to support the computational loads required, or it is not desirable to perform the functions onboard in the flight test program for other reasons. Second, remote computational support either required or highly desirable to conduct flight testing itself was addressed. The use is proposed of an Automated Flight Management System which is described in conceptual detail. Third, autonomous operations is discussed and finally, unmanned operations

Kesan penggunaan prosedur pembelajaran kawalan kendiri (self-regulated learning) terhadap pencapaian akademik, kemahiran meta kognitif dan motivasi pelajar politeknik : kajian kes

Pembelajaran Kawalan Kendiri (PKK) merupakan satu strategi pembelajaran efektif yang membantu pelajar untuk kompeten dan mempunyai autonomi dalam diri. Namun, prosedur yang betul bagi mengaplikasikan PKK masih memerlukan penambahbaikan disebabkan oleh pelajar cepat bosan belajar subjek teori. Pelajar juga didapati mengamalkan surface learning, mudah hilang fokus dalam kelas dan tidak cekap dalam mengawal kemahiran meta kognitif. Oleh itu, tujuan kajian ini dilaksanakan adalah untuk membangunkan satu prosedur PKK khusus untuk pelajar politeknik yang mengambil subjek Prinsip Pengurusan dan diuji keberkesanannya terhadap pencapaian akademik, kemahiran meta kognitif dan motivasi pelajar. Terdapat dua (2) fasa telah digunakan dalam kajian ini. Fasa pertama (1) ialah pembangunan prosedur PKK menggunakan analisis dokumen dan model Kemp. Analisis frekuensi telah digunakan dalam fasa ini. Terdapat tiga (3) hasil dapatan kajian daripada fasa pembangunan iaitu Prosedur PKK, Aktiviti Pengajaran dan Rancangan Pengajaran Harian (RPH). Fasa kedua (2) ialah pelaksanaan prosedur PKK menggunakan reka bentuk kuasi eksperimen iaitu ujian pra-pasca bagi kumpulan-kumpulan tidak seimbang. 43 orang pelajar Politeknik Sultan Haji Ahmad Shah (POLISAS) telah dipilih sebagai kumpulan rawatan manakala 38 orang pelajar Politeknik Merlimau (PMM) sebagai kumpulan kawalan. Analisis deskriptif skor min dan analisis inferensi MANCOVA telah digunakan dalam kajian ini bagi menguji perbezaan antara kumpulan kajian. Berdasarkan hasil analisis MANCOVA yang telah dijalankan, didapati wujud perbezaan yang signifikan secara statistik antara kumpulan rawatan dan kawalan bagi pencapaian akademik [F (1, 76) = 24.786, p = .000], kemahiran meta kognitif [F (1, 76) = 14.864, p = .000] dan motivasi [F (1, 76) = 65.148, p = .000]. Kesimpulannya, prosedur PKK terbukti berkesan dan boleh dijadikan panduan kepada pensyarah dalam mengaplikasikan PKK dengan lebih efektif dan berkesan

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Quadrotor control for persistent surveillance of dynamic environments

Thesis (M.S.)--Boston UniversityThe last decade has witnessed many advances in the field of small scale unmanned aerial vehicles (UAVs). In particular, the quadrotor has attracted significant attention. Due to its ability to perform vertical takeoff and landing, and to operate in cluttered spaces, the quadrotor is utilized in numerous practical applications, such as reconnaissance and information gathering in unsafe or otherwise unreachable environments. This work considers the application of aerial surveillance over a city-like environment. The thesis presents a framework for automatic deployment of quadrotors to monitor and react to dynamically changing events. The framework has a hierarchical structure. At the top level, the UAVs perform complex behaviors that satisfy high- level mission specifications. At the bottom level, low-level controllers drive actuators on vehicles to perform the desired maneuvers. In parallel with the development of controllers, this work covers the implementation of the system into an experimental testbed. The testbed emulates a city using physical objects to represent static features and projectors to display dynamic events occurring on the ground as seen by an aerial vehicle. The experimental platform features a motion capture system that provides position data for UAVs and physical features of the environment, allowing for precise, closed-loop control of the vehicles. Experimental runs in the testbed are used to validate the effectiveness of the developed control strategies

LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks

In this work, a novel learning-based approach has been developed to generate driving paths by integrating LIDAR point clouds, GPS-IMU information, and Google driving directions. The system is based on a fully convolutional neural network that jointly learns to carry out perception and path generation from real-world driving sequences and that is trained using automatically generated training examples. Several combinations of input data were tested in order to assess the performance gain provided by specific information modalities. The fully convolutional neural network trained using all the available sensors together with driving directions achieved the best MaxF score of 88.13% when considering a region of interest of 60x60 meters. By considering a smaller region of interest, the agreement between predicted paths and ground-truth increased to 92.60%. The positive results obtained in this work indicate that the proposed system may help fill the gap between low-level scene parsing and behavior-reflex approaches by generating outputs that are close to vehicle control and at the same time human-interpretable.Comment: Changed title, formerly "Simultaneous Perception and Path Generation Using Fully Convolutional Neural Networks