Abstract
The efficient application of phytochemical products in agriculture is a complex issue that demands optimised sprayers and variable rate technologies, which rely on advanced sensing systems to address challenges such as overdosage and product losses. This work developed a system capable of processing different tree canopy parameters to support precision fruit farming and environmental protection using intelligent spraying methodologies. This system is based on a 2D light detection and ranging (LiDAR) sensor and a Global Navigation Satellite System (GNSS) receiver integrated into a sprayer driven by a tractor. The algorithm detects the canopy boundaries, allowing spray only in the presence of vegetation. The spray volume spared evaluates the system's performance compared to a Tree Row Volume (TRV) methodology. The results showed a 28% reduction in the overdosage of spraying product. The second step in this work was calculating and adjusting the amount of liquid to apply based on the tree volume. Considering this parameter, the saving obtained had an average value for the right and left rows of 78%. The volume of the trees was also monitored in a georeferenced manner with the creation of a occupation grid map. This map recorded the trajectory of the sprayer and the detected trees according to their volume.

Abstract
[No abstract available]

Abstract
Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios.

Abstract
Long-term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long-term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long-term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long-term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co-occurrence to identify the terms more used in long-term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

Abstract
The pose estimation of a mobile robotic system is essential in many autonomous applications. Inertial sensors provide high-frequency measurements that can be used to estimate the displacement, however, for estimating the orientation, an additional filter is required. Some of the newest Attitude and Heading Reference Systems can provide a referenced estimation of the orientation of the device, allowing it to retrieve the orientation of a robotic system. However, magnetic field perturbations caused by ferromagnetic objects or induced magnetic fields might influence these systems and, consequently, lead to the accumulation of errors over time. In this paper, the performance of the Xsens fusion filter is compared with a stateof-the-art algorithm to estimate the orientation of the system under dynamic movements and in the presence of magnetic perturbations, with the goal of finding the most suitable for an Unmanned Aerial Vehicle. The results show that both filters are robust and perform well in the target scenario, with a root mean squared error between 2 and 5 degrees; however, the Xsens fusion filter does not require an extra computer to process the data.

Abstract
Quadruped robots are gaining attention in the research community because of their superior mobility and versatility in a wide range of applications. However, they are restricted to procedures that do not need precise object interaction. With the addition of a robotic arm, they can overcome these drawbacks and be used in a new set of tasks. Combining a legged robot's dextrous movement with a robotic arm's maneuverability allows the emergence of a highly flexible system, but with the disadvantage of higher complexity of motion planning and control methods. This paper gives an overview of the existing quadruped systems capable of manipulation, with a particular interest in systems with high movement flexibility. The main topics discussed are the motion planning approaches and the selected kinematic configuration. This review concludes that the most followed research path is to add a robotic arm on the quadrupedal base and that the motion planning approach used depends on the desired application. For simple tasks, the arm can be seen as an independent system, which is simpler to implement. For more complex jobs the coupling effects between the arm and quadruped robot must be considered.