The increasing focus on assistance robots for aging populations in domestic settings highlights the critical need for solutions that ensure a good quality of life. One of the main challenges faced by these robots is the ability to understand complex indoor environments, which often contain various obstacles, including dynamic ones, and to identify specific objects or workspaces for effective manipulation and navigation. However, access to realistic domestic environments for testing is limited, making it difficult for researchers to obtain physical models that accurately reflect these conditions. This article presents a solution for collecting data from domestic environments through simulations using a randomized environment generation algorithm, data acquisition, and classification. This approach aims to compensate for the lack of accessible and versatile domestic environments available online for testing and improving the capabilities of assistive domestic robots. The results obtained demonstrate a high success rate in creating simulated data that can effectively applied to real robots and a high level of confidence from a neural network retrained with synthetic data.
This paper proposes a software architecture for monitoring and diagnostics of failures in industrial systems. The architecture aims to support the operator's decision-making process by enabling a real-time and intuitive understanding of system faults. The paper describes the methodology and implementation process applied to a real industrial case: a welding collaborative robotic application. However, the proposed software architecture can be easily extended to a broader number of industrial systems. The core of the idea is based on an ecosystem of Internet of Things (IoT) elements deployed in the automation systems that collect the system status and alarms to stream them to the cloud server. The industrial use case described in the paper is a collaborative robot-assisted welding solution for automated MIG/MAG welding produced by “Indus-tria Tecnologica Italiana”, an Italian SME company, with the brand name”MyWelder”. We investigated the system's impact on the operator's work and its effectiveness in supporting his/her decision-making process. Additionally, the validation process assessed the system's functionalities within this specific use case. The primary objective related to the use case is to establish a strategy that minimizes the production of defective parts, ultimately reducing waste.
This work presents the design of a multi-axis force-torque sensor based on strain gauges located on its planar surfaces (frontal and back sides) for easy installation, to measure external forces in a direct and simple way, due to physical interactions between the mobile platform of a humanoid robot and the environment (objects and humans). This is an outstanding feature, because the estimation of those interaction forces will allow the humanoid robot to execute manipulation tasks using an unusual body part (mobile platform) and to distinguish between intentional and unintentional contacts known as gesture recognition, and at the same time, guarantying a safe interaction between a wheeled humanoid robot and the environment. All the aforementioned is achieved while the mobile platform is carrying a heavy load (the upper body of the humanoid robot). The force-torque sensor was designed using a finite element analysis (FEA), and the results show that it is possible to distinguish each component of the wrench (F) over bar with a low coupling, having a good isotropy and a high sensitivity, due a suitable and strategic composition of the Wheatstone bridge circuits.
Traffic accidents are a major concern in the current mobility context, both for driverless and human driven vehicles. In this line, the early assessment of collision risk is paramount in order to predict and avoid accidents and casualties involving the targets in the road. For that purpose, this paper proposes a solution of creating continuously updated risk maps around the ego vehicle, along with some risk indicators to assess the risk of collision before it occurs. Those maps are based on the properties of the targets perceived by the sensors onboard the vehicle, along with models of motion and occupancy prediction. This domain of risk prediction and assessment is very vast, and this paper focuses on combining visual perception for the classification of targets, and 3D LiDAR to extract the relative location, dimensions and motion properties of the targets. This information is fed into probabilistic occupancy maps and risk maps are created at the rate of sensor data stream, both as visual information for humans, but also as risk descriptors to trigger alert and emergency actions. The results in simulation and in real scenarios show consistent and valid information that can be further used in real scenarios on the road.
This paper discusses the emerging field of robotics, particularly focusing on motion planning for robotic manipulators. It highlights the need for simplification and standardization in robot implementation processes. Among several tools available, the paper focuses on the MoveIt tool due to its compatibility, popularity, and community contributions. However, the paper acknowledges some resistance in developing new applications with MoveIt, especially for researchers and beginners. To address this, the paper introduces an efficient, modular action server for interacting with the MoveIt framework. This pipeline simplifies parameter reconfiguration and provides a general solution for the motion planning problem. It can calculate trajectories for robotic manipulators without environmental collisions using a single server request and supports operation in different modes. The server was tested on an Universal Robots UR10 manipulator, demonstrating its ability to quickly plan paths for two test operations: an object pick-and-place mission and a collision avoidance test. The results were positive, achieving the set goals with minimal user-server interaction. This work represents a significant step towards more efficient and user-friendly robotic manipulation.
