Abstract
The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems. © 2021 IEEE.

Abstract
Robotics competitions are environments that foster teamwork, AI, and technology development by encouraging students, researchers, and academics to test their solutions against each other. These competitions often challenge the competitors' prototypes with tasks specifically designed to benchmark them with the current optimal solutions. During the prototype stages of a robot, the development costs and time spent are often higher than other stages, as changes in the prototype are frequent. Simulation is often used to reduce these variables as it allows flexibility in all development stages before transitioning to the real scenario. However, a digital twin can be used to increase even further flexibility and effectiveness. Digital twins are virtual representations of real assets, providing replication and prediction of real scenario events, and real-time monitoring of the real object. Thus, this paper presents the development of a digital twin of an automatic guided vehicle (AGV) to the Robot@Factory Lite competition and the tests performed to validate the approach.

Abstract
Nowadays, a laboratory operator in the areas of chemistry, biology or medicine spends considerable time performing micropipetting procedures, a common, monotonous and repetitive task which compromises the ergonomics of individuals, namely related to wrist musculoskeletal disorders. In this work, the design of a kinesthetic teaching approach for automating the micropipetting technique is presented, allowing to redirect the operator to other non-repetitive tasks, aiming to reduce the exposure to ergonomic risks. The proposed robotic solution has an innovative gripping system capable of supporting, actuating and regulating the volume of a manual micropipette. The system is able to configure the position of diverse laboratory materials, such as lab containers and plates, on the workbench through a collaborative robotic arm, providing flexibility to adapt to different procedures. A projected human-machine interface, which combines the display of information on the workbench with an infrared based interaction device was developed, providing a more intuitive interaction between the operator and the system during the configuration and operation phases. In contrast to the majority of the existing liquid handling systems, the proposed system allows the operator to place the materials freely on the workbench and the usage of different materials' variants, facilitating the implementation of the system in any laboratory. The attained performance and ease of use of the system were very encouraging since all the defined tasks in the conducted experiments were successfully performed by users with minimum training, highlighting its potential inclusion in the laboratory routine panorama.

Abstract
Autonomous Robots with multiple directional thrusters are normally over-actuated systems that require nonlinear control allocation methods to map the forces that drive the robot's dynamics and act as virtual control variables to the actuators. This process demands computational efforts that, sometimes, are not available in small robotic platforms. The present paper introduces a new control allocation approach with fast convergence, high accuracy, and dealing with complex nonlinear problems, especially in embedded systems. The adopted approach divides the desired nonlinear system into coupled linear problems. For that purpose, the Real Actions (RAs) and Virtual Control Variables (VCVs) are broke in two or more sets each. While the RA subsets are designed to linearize the system according to different input subspaces, the VCV is designed to be partially coupled to overlap the output subspaces. This approach generates smaller linear systems with fast and robust convergence used sequentially to solve nonlinear allocation problems. This methodology is assessed in mathematical tutorial cases and over-actuated UAV simulations.

Abstract
With the continuously growing usage of collaborative robots in industry, the need for achieving a seamless human-robot interaction has also increased, considering that it is a key factor towards reaching a more flexible, effective, and efficient production line. As a prominent and prospective tool to support the human operator to understand and interact with robots, Augmented Reality (AR) has been employed in numerous human-robot collaborative and cooperative industrial applications. Therefore, this systematic literature review critically appraises 32 papers' published between 2016 and 2021 to identify the main employed AR technologies, outline the current state of the art of augmented reality for human-robot collaboration and cooperation, and point out future developments for this research field. Results suggest that this is still an expanding research field, especially with the advent of recent advancements regarding head-mounted displays (HMDs). Moreover, projector-based and HMDs developed approaches are showing promising positive influences over operator-related aspects such as performance, task awareness, and safety feeling, even though HMDs need further maturation in ergonomic aspects. Further research should focus on large-scale assessment of the proposed solutions in industrial environments, involving the solution's target audience, and on establishing standards and guidelines for developing AR assistance systems.

Abstract
This paper studies the state-of-the-art of active perception solutions for manipulation in agriculture and suggests a possible architecture for an active perception system for harvesting in agriculture. Research and developing robots for agricultural context is a challenge, particularly for harvesting and pruning context applications. These applications normally consider mobile manipulators and their cognitive part has many challenges. Active perception systems look reasonable approach for fruit assessment robustly and economically. This systematic literature review focus in the topic of active perception for fruits harvesting robots. The search was performed in five different databases. The search resumed into 1034 publications from which only 195 publications where considered for inclusion in this review after analysis. We conclude that the most of researches are mainly about fruit detection and segmentation in two-dimensional space using evenly classic computer vision strategies and deep learning models. For harvesting, multiple viewpoint and visual servoing are the most commonly used strategies. The research of these last topics does not look robust yet, and require further analysis and improvements for better results on fruit harvesting.