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This paper describes the development of a mobile agricultural robot capable of performing high-capacity transport tasks within greenhouses in presence of people or other agricultural machines. The main objective is to provide the robot with enough technology to work collaboratively with nearby human workers. In addition, the robot must also be able to transport 100 kilograms in a safe way over uneven terrain, a characteristic not usually found in existing greenhouse robots. This is important to ensure the sustainability of intensive greenhouse cultivation, as it is essential to allow more flexible use of robots when adapting. This would allow for expanding infrastructure size and operating volume to suit different greenhouse conditions, thus maximizing production. The robot is fitted with different sensors to enable autonomous navigation, perception, and to identify the environment and the operators (3D LiDAR, stereo cameras, and ultrasound). It also features the hardware necessary for cloud connection to share data in real time. All sensors have been validated to work correctly, hence the robot can move around the greenhouse. With the software currently used for collaborative robotics, the ultrasounds correctly identify the environment, and cameras and LiDAR can locate the farmer correctly. In this work, several gaps in greenhouse robotics are addressed by designing, developing, and validating a collaborative mobile robot with advanced sensors and algorithms with IoT integration. The robot lays the foundation for the implementation of autonomous navigation, collaborating with farmers in real-time and efficient operation in complex greenhouse environments, laying the groundwork for future advances in agricultural automation.

Abstract Emotional facial expression recognition is a key ability for adequate social functioning. The current study aims to test if the differential outcomes procedure (DOP) may improve the recognition of dynamic facial expressions of emotions and to further explore whether schizotypal personality traits may have any effect on performance. 183 undergraduate students completed a task where a face morphed from a neutral expression to one of the six basic emotions at full intensity over 10 s. Participants had to press spacebar as soon as they identified the emotion and choose which had appeared. In the first block, participants received no outcomes. In the second block, a group received specific outcomes associated to each emotion (DOP group), while another group received non-differential outcomes after correctly responding (NOP group). Employing generalized linear models (GLMs) and Bayesian inference we estimated different parameters to answer our research goals. Schizotypal personality traits did not seem to affect dynamic emotional facial expression recognition. Participants of the DOP group were less likely to respond incorrectly to faces showing Fear and Surprise at fewer intensity levels. This may suggest that the DOP could lead to better identification of the main features that differentiate each facial expression of emotion.

This paper presents an innovative dataset designed explicitly for challenging agricultural environments, such as greenhouses, where precise location is crucial, but GNNS accuracy may be compromised by construction elements and the crop. The dataset was collected using a mobile platform equipped with a set of sensors typically used in mobile robots as it was moved through all the corridors of a typical Mediterranean greenhouse featuring tomato crops. This dataset presents a unique opportunity for constructing detailed 3D models of plants in such indoor-like spaces, with potential applications such as robotized spraying. For the first time, to the authors’ knowledge, a dataset suitable to test simultaneous localization and mapping (SLAM) methods is presented in a greenhouse environment, which poses unique challenges. The suitability of the dataset for this purpose is assessed by presenting SLAM results with state-of-the-art algorithms. The dataset is available online.

Development of applications related to closed-loop control requires either testing on the field or on a realistic simulator, with the latter being more convenient. To ease that need, this work introduces MVSim, a simulator for multiple vehicles or robots capable of running in real time dozens of agents in simple scenarios, or a handful of them in complex scenarios. MVSim employs realistic physics-grounded friction models for tire–ground interaction, and aims at accurate and GPU-accelerated simulation of most common modern sensors employed in mobile robotics and autonomous vehicle research, such as depth and RGB cameras, or 2D and 3D LiDAR scanners. All depth-related sensors are able to accurately measure distances to 3D models provided by the user to define custom world elements. Efficient simulation is achieved by means of focusing on ground vehicles, which allows the use of a simplified 2D physics engine for body collisions while solving wheel–ground interaction forces separately. The core parts of the system are written in C++ for maximum efficiency, while Python, ROS 1, and ROS 2 wrappers are also offered. A custom publish/subscribe protocol based on ZeroMQ (ZMQ) is defined to allow for multiprocess applications to access or modify a running simulation. This simulator enables and makes easier to do research and development on vehicular dynamics, autonomous navigation algorithms, and simultaneous localization and mapping (SLAM) methods. An experimental performance benchmarking is provided against other state-of-the-art simulators showing significant less CPU usage. The project source code is freely available online under the BSD 3-clause license in https://github.com/MRPT/mvsim.

The broad autism phenotype is the phenotypic expression of the primary characteristics of autism. However, currently available tests do not agree with the two-domain operationalization of broad autism phenotype or autism, and their internal structure has shown instability across applications. This study presents the Broad Autism Phenotype--International Test, the first such test designed to target a two-domain operationalization in Spain and the United Kingdom. A broad pilot study was conducted in three community samples, two from Spain (ES1, N = 970; ES2, N = 460) and one from the United Kingdom (N = 530). The Broad Autism Phenotype--International Test consists of 20 items distributed into the SOCIAL-BAP and RIRE-BAP dimensions, representing the seven autistic subdomains. Common factor approaches were used to find a simple and invariant factor structure between countries. The Broad Autism Phenotype--International Test presents full metric invariance between two Spanish samples, partial invariance between countries, and adequate omega reliability estimation. Evidence-based results on the relationships of the SOCIAL-BAP and RIRE-BAP to other variables (the Social Responsiveness Scale-2 and the Broad Autism Phenotype Questionnaire) support the interpretation of its scores for the intended uses. The evidence provided in this article shows that the Broad Autism Phenotype--International Test functions adequately compared to other available broad autism phenotype tests, such as the Broad Autism Phenotype Questionnaire.