We propose SiM3D, the first benchmark considering the integration of multiview and multimodal information for comprehensive 3D anomaly detection and segmentation (ADS), where the task is to produce a voxel-based Anomaly Volume. Moreover, SiM3D focuses on a scenario of high interest in manufacturing: single-instance anomaly detection, where only one object, either real or synthetic, is available for training. In this respect, SiM3D stands out as the first ADS benchmark that addresses the challenge of generalising from synthetic training data to real test data. SiM3D includes a novel multimodal multiview dataset acquired using top-tier industrial sensors and robots. The dataset features multiview high-resolution images (12 Mpx) and point clouds (7M points) for 333 instances of eight types of objects, alongside a CAD model for each type. We also provide manually annotated 3D segmentation GTs for anomalous test samples. To establish reference baselines for the proposed multiview 3D ADS task, we adapt prominent singleview methods and assess their performance using novel metrics that operate on Anomaly Volumes.

Safety can be listed as one of the most important aspects of every working environment, from the low risky to the most dangerous one. Construction sites can be clearly identified among the riskiest working fields, mainly because several complex and fast maneuvers are executed in a very crowded, dynamic and uncertain scenario. Specifically referring to construction machines, typical operations such as digging, earthmoving, heavy weights loading/unloading, etc. become incredibly risky if the machine stability is not guaranteed by the operator. To this purpose, this paper proposes a practical implementation of a dynamic fall prediction system for excavators based on the concept of the Zero Moment Point (ZMP). A commercially available excavator has been dynamically modelled and equipped with a set of sensors to reconstruct the state of the machine. When the proposed system is running, an alarm rings if a critical condition is reached, thus allowing the operator to avoid risky and potentially harmful situations.

In this paper, the process of modelling and identification of a hydraulic actuator is discussed. In this framework a simple model based on the classical theory has been derived and a first experimental campaign has been performed on a test bench. These tests highlighted the presence of unmodeled phenomena (e.g. dead-zone, hysteresis, etc.), therefore a second and more extensive experimental campaign has been done. With the acquired knowledge an improved model has been developed and its parameters identified. Finally several experimental tests have been performed in order to validate the model.

In an industiral scenario the capability to detect and track human workers entering a robotic cell represents a fundamental requirement to enable safe and efficient human-robot cooperation. This paper proposes a new approach to the problem of Human Detection and Tracking based on low-cost commercial RGB surveillance cameras, image warping techniques, computer vision algorithms, efficient data structures such as k-dimensional trees and particle filtering. Results of several validation experiments are presented.

In the past few years the need for more flexibility in industrial production has implied a growing attention towards scenarios where humans work directly in touch with robots. In order to allow safe human-robot interaction, a methodology to evaluate the severity of an impact between a human worker and an industrial robot, based on related work on injury knowledge in human-robot contacts and relying on information coming from different exteroceptive sensors, has been developed in this paper. On the basis of this severity evaluation, the robot controller enforces a suitable safety-oriented strategy, ranging from on-path speed reduction to task-consistent evasive motion and protective stop. The safety evaluation methodology has been implemented in a dedicated software component, integrated with a video surveillance system and with the real time robot controller to obtain a complete HW/SW architecture named “Safety Controller”. The system has been validated on an ABB IRB140 robot.