Abstract
Directed Energy Deposition (DED) is a free-form metal additive manufacturing process characterized as toolless, flexible, and energy-efficient compared to traditional processes. However, it is a complex system with a highly dynamic nature that presents challenges for modeling and optimization due to its multiphysics and multiscale characteristics. Additionally, multiple factors such as different machine setups and materials require extensive testing through single-track depositions, which can be time and resource-intensive. Single-track experiments are the foundation for establishing optimal initial parameters and comprehensively characterizing bead geometry, ensuring the accuracy and efficiency of computer-aided design and process quality validation. We digitized a DED setup using the Robot Operating System (ROS 2) and employed a thermal camera for real-time monitoring and evaluation to streamline the experimentation process. With the laser power and velocity as inputs, we optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM). The three-objective approach achieved better rewards in all iterations and, when implemented in areal setup, allowed to reduce the number of experiments and shorten setup time. Our approach can minimize waste, increase the quality and reliability of DED, and enhance and simplify human-process interaction by leveraging the collaboration between human knowledge and model predictions.

Abstract
Pruning consists on an agricultural trimming procedure that is crucial in some species of plants to promote healthy growth and increased yield. Generally, this task is done through manual labour, which is costly, physically demanding, and potentially dangerous for the worker. Robotic pruning is an automated alternative approach to manual labour on this task. This approach focuses on selective pruning and requires the existence of an end-effector capable of detecting and cutting the correct point on the branch to achieve efficient pruning. This paper reviews and analyses different end-effectors used in robotic pruning, which helped to understand the advantages and limitations of the different techniques used and, subsequently, clarified the work required to enable autonomous pruning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
In nature, and particularly in agriculture, pollination is fundamental for the sustainability of our society. In this context, pollination is a vital process underlying crop yield quality and is responsible for the biodiversity and the standards of the flora. Bees play a crucial role in natural pollination; however, their populations are declining. Robots can help maintain pollination levels while humans work to recover bee populations. Swarm robotics approaches appear promising for robotic pollination. This paper proposes the cooperation between multiple Unmanned Aerial Vehicles (UAVs) and an Unmanned Ground Vehicle (UGV), leveraging the advantages of collaborative work for pollination, referred to as Pollinationbots. Pollinationbots is based in swarm behaviors and methodologies to implement more effective pollination strategies, ensuring efficient pollination across various scenarios. The paper presents the architecture of the Pollinationbots system, which was evaluated using the Webots simulator, focusing on path planning and follower behavior. Preliminary simulation results indicate that this is a viable solution for robotic pollination. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
With the serious danger to nature and humanity that forest fires are, taken into consideration, this work aims to develop an artificial intelligence model capable of accurately predicting the forest fire risk in a certain region based on four different factors: temperature, wind speed, rain and humidity. Thus, three models were created using three different approaches: Artificial Neural Networks (ANN), Random Forest (RF), and K-Nearest Neighbor (KNN), and making use of an Algerian forest fire dataset. The ANN and RF both achieved high accuracy results of 97%, while the KNN achieved a slightly lower average of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Reliable operation of production lines without unscheduled disruptions is of paramount importance for ensuring the proper operation of automated working cells involving robotic systems. This article addresses the issue of preventing disruptions to an automotive production line that can arise from incorrect placement of aluminum car parts by a human operator in a feeding container with 4 indexing pins for each part. The detection of the misplaced parts is critical for avoiding collisions between the containers and a high pressure washing machine and also to avoid collisions between the parts and a robotic arm that is feeding parts to a air leakage inspection machine. The proposed inspection system relies on a 3D sensor for scanning the parts inside a container and then estimates the 6 DoF pose of the container followed by an analysis of the overlap percentage between each part reference point cloud and the 3D sensor data. When the overlap percentage is below a given threshold, the part is considered as misplaced and the operator is alerted to fix the part placement in the container. The deployment of the inspection system on an automotive production line for 22 weeks has shown promising results by avoiding 18 hours of disruptions, since it detected 407 containers having misplaced parts in 4524 inspections, from which 12 were false negatives, while no false positives were reported, which allowed the elimination of disruptions to the production line at the cost of manual reinspection of 0.27% of false negative containers by the operator.

Abstract
Forest soil ripping is a practice that involves revolving the soil in a forest area to prepare it for planting or sowing operations. Advanced sensing systems may help in this kind of forestry operation to assure ideal ripping depth and intensity, as these are important aspects that have potential to minimise the environmental impact of forest soil ripping. In this work, a cost-effective contactless system - capable of detecting and mapping soil ripping depth in real-time - was developed and tested in laboratory and in a realistic forest scenario. The proposed system integrates two single-point LiDARs and a GNSS sensor. To evaluate the system, ground-truth data was manually collected on the field during the operation of the machine with a ripping implement. The proposed solution was tested in real conditions, and the results showed that the ripping depth was estimated with minimal error. The accuracy and mapping ripping depth ability of the low-cost sensor justify their use to support improved soil preparation with machines or robots toward sustainable forest industry.