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Transmission line modelling is a technique that can be applied to the analysis and design of electromagnetic devices. Developments to the method are described which improve its efficiency and flexibility.

A super‐condensed TLM node for modelling inhomogeneous anisotropic media on an arbitrarily graded mesh without using stubs is developed. It requires less storage, computationally is more efficient and can operate on a higher time step than existing TLM nodes. Complete derivation, implementation and validation of the new node are presented in the paper.

This paper aims to propose a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on the ground using the Stereo–inertial measurement unit (IMU) system. This method reparametrizes the pose according to the motion characteristics of TWIP and considers the impact of uneven ground on vision and IMU, which is more adaptable to the real environment.

When TWIP moves, it is constrained by the ground and swings back and forth to maintain balance. Therefore, the authors parameterize the robot pose as SE(2) pose plus pitch according to the motion characteristics of TWIP. However, the authors do not omit disturbances in other directions but perform error modeling, which is integrated into the visual constraints and IMU pre-integration constraints as an error term. Finally, the authors analyze the influence of the error term on the vision and IMU constraints during the optimization process. Compared to traditional algorithms, the algorithm is simpler and better adapt to the real environment.

The results of indoor and outdoor experiments show that, for the TWIP robot, the method has better positioning accuracy and robustness compared with the state-of-the-art.

The algorithm in this paper is proposed for the localization and mapping of a TWIP robot. Different from the traditional positioning method on SE(3), this paper parameterizes the robot pose as SE(2) pose plus pitch according to the motion of TWIP and the motion disturbances in other directions are integrated into visual constraints and IMU pre-integration constraints as error terms, which simplifies the optimization parameters, better adapts to the real environment and improves the accuracy of positioning.

The purpose of this paper is to study the factors that act as innovation obstacles in precision agriculture (PA) technologies in Argentina, one of the world leading exporters of cereals and oilseeds. The focus of this study is on the supply side, i.e. the factors that are perceived by PA firms as obstacles for the expansion of their market.

Based on a survey to 67 firms that develop PA technologies in Argentina, this study examines the impact of different types of obstacles on firms’ growth and innovation activities. This analysis is complemented with the results that emerge from a series of interviews with different stakeholders (such as firms’ managers, policymakers and experts).

In this study, it was determined that market and cost factors negatively affect firms’ growth, while institutional obstacles reduce the amount of innovation efforts. In turn, knowledge barriers positively impact on the relevance firms assigned to R&D activities. This study helps identify different strategies that firms have put in place to overcome the barriers they face. Finally, policy implications of the results are discussed.

PA technologies may contribute to greening agricultural production and offer an opportunity for the emergence of domestic suppliers of innovative equipment and services based on the use of data science, artificial intelligence and Internet of Things. To the bets of the authors’ knowledge, this is the first study that explores the obstacles that prevent growth and impact on innovation activities of PA firms. The insights from this study are valuable for both researchers and policymakers aiming to foster emergence of high-tech clusters in developing countries.

El propósito de este trabajo es estudiar los factores que actúan como obstáculos a la innovación en tecnologías emergentes, tomando el caso de la agricultura de precisión en Argentina.

Estudiamos el caso de la agricultura de precisión en Argentina combinando técnicas econométricas y análisis cualitativo. Sobre la base de una encuesta a 67 empresas que desarrollan tecnologías de agricultura de precisión en Argentina, examinamos el impacto de diferentes tipos de obstáculos en el crecimiento de las empresas y las actividades de innovación. Este análisis se complementa con hallazgos derivados de una serie de entrevistas con diferentes actores (gerentes de empresas, responsables de políticas, expertos, agricultores, etc.).

Encontramos que los obstáculos de mercado y de costos afectan negativamente el crecimiento de las empresas, mientras que los obstáculos institucionales reducen la cantidad de esfuerzos de innovación. A su vez, las barreras de conocimiento tienen un impacto positivo en la relevancia que las empresas asignan a las actividades de I + D. También identificamos diferentes estrategias que las empresas han puesto en marcha para superar las barreras que enfrentan. Finalmente, discutimos las implicaciones de política de nuestros hallazgos.

Las tecnologías de AP contribuyen a una agricultura sustentable y ofrecen una oportunidad al surgimiento de proveedores locales de equipamiento y servicios, basados en ciencia de datos, inteligencia artificial e Internet de las Cosas. Para los autores, este es el primer estudio que explora los obstáculos al crecimiento y a la innovación en firmas de AP. Las contribuciones de este estudio son relevantes tanto para futuras investigaciones como para hacedores de políticas interesados en promover el surgimiento de clusters high-tech en países en desarrollo.

Obstáculos a la innovación, Política de innovación, Agricultura de precisión

O objetivo deste artigo é estudar os fatores que atuam como obstáculos à inovação em tecnologias emergentes, tomando o caso da agricultura de precisão na Argentina.

O caso da agricultura de precisão na Argentina é estudado combinando técnicas econométricas e análises qualitativas. Com base numa pesquisa com 67 empresas que desenvolvem tecnologias de agricultura de precisão na Argentina, examinamos o impacto de diferentes tipos de obstáculos nas atividades de crescimento e inovação das empresas. Esta análise é complementada com as conclusões que emergem de uma série de entrevistas com diferentes partes interessadas (gestores das empresas, responsáveis políticos, especialistas, agricultores, etc.).

Descobrimos que os fatores de mercado e custo afetam negativamente o crescimento das empresas, enquanto os obstáculos institucionais reduzem a quantidade de esforços de inovação. Além do mais, as barreiras do conhecimento impactam positivamente na relevância que as empresas atribuem às atividades de P & D. Também identificamos diferentes estratégias que as empresas implementaram para superar as barreiras que enfrentam. Finalmente, discutimos as implicações políticas de nossos resultados.

As tecnologias de AP contribuem para a agricultura sustentável e oferecem uma oportunidade para o surgimento de fornecedores locais de equipamentos e serviços, baseados na ciência de dados, inteligência artificial e na Internet das Coisas. Para os autores, este é o primeiro estudo que explora os obstáculos ao crescimento e inovação nas firmas de PA. As contribuições deste estudo são relevantes tanto para pesquisas futuras quanto para formuladores de políticas interessados em promover o surgimento de clusters de alta tecnologia em países em desenvolvimento.

Obstáculos à inovação, Política de inovação, Agricultura de precisão

This paper aims to present a method for improving the performance of the visual-inertial navigation system (VINS) by using a bio-inspired polarized light compass.

The measurement model of each sensor module is derived, and a robust stochastic cloning extended Kalman filter (RSC-EKF) is implemented for data fusion. This fusion framework can not only handle multiple relative and absolute measurements, but can also deal with outliers, sensor outages of each measurement module.

The paper tests the approach on data sets acquired by a land vehicle moving in different environments and compares its performance against other methods. The results demonstrate the effectiveness of the proposed method for reducing the error growth of the VINS in the long run.

The main contribution of this paper lies in the design/implementation of the RSC-EKF for incorporating the homemade polarized light compass into visual-inertial navigation pipeline. The real-world tests in different environments demonstrate the effectiveness and feasibility of the proposed approach.

To realize stable and precise localization in the dynamic environments, the authors propose a fast and robust visual odometry (VO) approach with a low-cost Inertial Measurement Unit (IMU) in this study.

The proposed VO incorporates the direct method with the indirect method to track the features and to optimize the camera pose. It initializes the positions of tracked pixels with the IMU information. Besides, the tracked pixels are refined by minimizing the photometric errors. Due to the small convergence radius of the indirect method, the dynamic pixels are rejected. Subsequently, the camera pose is optimized by minimizing the reprojection errors. The frames with little dynamic information are selected to create keyframes. Finally, the local bundle adjustment is performed to refine the poses of the keyframes and the positions of 3-D points.

The proposed VO approach is evaluated experimentally in dynamic environments with various motion types, suggesting that the proposed approach achieves more accurate and stable location than the conventional approach. Moreover, the proposed VO approach works well in the environments with the motion blur.

The proposed approach fuses the indirect method and the direct method with the IMU information, which improves the localization in dynamic environments significantly.

This paper aims to propose an environmentally adaptive trajectory planning system considering the dynamic characteristics of unmanned aerial vehicles (UAVs) and the distance between obstacles and the UAV. The system generates a smooth and safe flight trajectory online.

First, the hybrid A* search method considering the dynamic characteristics of the quadrotor is used to find the collision-free initial trajectory. Then, environmentally adaptive velocity cost is designed for environment-adaptive trajectory optimization using environmental gradient data. The proposed method adaptively adjusts the autonomous flight speed of the UAV. Finally, the initial trajectory is applied to the multi-layered optimization framework to make it smooth and dynamically viable.

The feasibility of the designed system is validated by online flight experiments, which are in unknown, complex situations.

The proposed trajectory planning system is integrated into a vision-based quadrotor platform. It is easily implementable onboard and computationally efficient.

A hybrid A* path searching method is proposed to generate feasible motion primitives by dispersing the input space uniformly. The proposed method considers the control input of the UAV and the search time as the heuristic cost. Therefore, the proposed method can provide an initial path with the minimum flying time and energy loss that benefits trajectory optimization. The environmentally adaptive velocity cost is proposed to adaptively adjust the flight speed of the UAV using the distance between obstacles and the UAV. Furthermore, a multi-layered environmentally adaptive trajectory optimization framework is proposed to generate a smooth and safe trajectory.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Typical feature-matching algorithms use only unary constraints on appearances to build correspondences where little structure information is used. Ignoring structure information makes them sensitive to various environmental perturbations. The purpose of this paper is to propose a novel graph-based method that aims to improve matching accuracy by fully exploiting the structure information.

Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.

The authors compare it with several state-of-the-art visual simultaneous localization and mapping algorithms on three datasets. Experimental results reveal that the ORB-G algorithm provides more accurate and robust trajectories in general.

Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.

This paper aims to present a new vision-based approach for both the identification and the estimation of the relative distance between the unmanned aerial vehicle (UAV) and power pylon. Autonomous power line inspection using small UAVs, has been the focus of many research works over the past couple of decades. Automatic detection of power pylons is a primary requirement to achieve such autonomous systems. It is still a challenging task due to the complex geometry and cluttered background of these structures.

The identification solution proposed, avoids the complexity of classic object recognition techniques. Instead of searching the whole image for the pylon template, low-level geometric priors with robust colour attributes are combined to remove the pylon background. The depth estimation, on the other hand, is based on a new concept which exploits the ego-motion of the inspection UAV to estimate its distance from the pylon using just a monocular camera.

An algorithm is tested on a quadrotor UAV, using different kinds of metallic power pylons. Both simulation and real-world experiments, conducted in different backgrounds and illumination conditions, show very promising results.

In the real tests carried out, the Inertial Navigation System (INS) of the vehicle was used to estimate its ego-motion. A more reliable solution should be considered for longer distances, by either fusing INS and global positioning system data or using visual navigation techniques such as visual odometry.

A simple yet efficient solution is proposed that allows the UAV to reliably identify the pylon, with still a low processing cost. Considering a monocular solution is a major advantage, given the limited payload and processing power of such small vehicles.