CEPB dataset: a photorealistic dataset to foster the research on bin picking in cluttered environments.

Several datasets have been proposed in the literature, focusing on object detection and pose estimation. The majority of them are interested in recognizing isolated objects or the pose of objects in well-organized scenarios. This work introduces a novel dataset that aims to stress vision algorithms in the difficult task of object detection and pose estimation in highly cluttered scenes concerning the specific case of bin picking for the Cluttered Environment Picking Benchmark (CEPB). The dataset provides about 1.5M virtually generated photo-realistic images (RGB + depth + normals + segmentation) of 50K annotated cluttered scenes mixing rigid, soft, and deformable objects of varying sizes used in existing robotic picking benchmarks together with their 3D models (40 objects). Such images include three different camera positions, three light conditions, and multiple High Dynamic Range Imaging (HDRI) maps for domain randomization purposes. The annotations contain the 2D and 3D bounding boxes of the involved objects, the centroids' poses (translation + quaternion), and the visibility percentage of the objects' surfaces. Nearly 10K separated object images are presented to perform simple tests and compare them with more complex cluttered scenarios tests. A baseline performed with the DOPE neural network is reported to highlight the challenges introduced by the novel dataset.

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network.

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.

Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling.

The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis.

Co-located haptic interaction for virtual USG exploration.

Ultrasonography is a widespread diagnostic technique that can take advantage of virtual reality for the purpose of training and rehearsal. The placement, orientation and body interaction of the probe is fundamental for the execution of the exploration. This paper introduces a virtual reality setup that employs visuo-haptic feedback for virtual ultrasonography. The haptic feedback is provided by desktop delta-like haptic interface with a 3D printed ultrasonography probe, and features haptic point-cloud rendering with implicit surface rendering. The visual feedback is provided by a Head Mounted Display that displays the virtual body, the probe and the operator's hand while not in contact with the probe. The system provides a co-located experience by means of precise calibration of the reference system allowing to synchronize the display of the hand and the probe with the location of the corresponding physical entities.

Portable haptic interface with omni-directional movement and force capability.

We describe the design of a new mobile haptic interface that employs wheels for force rendering. The interface, consisting of an omni-directional Killough type platform, provides 2DOF force feedback with different control modalities. The system autonomously performs sensor fusion for localization and force rendering. This paper explains the relevant choices concerning the functional aspects, the control design, the mechanical and electronic solution. Experimental results for force feedback characterization are reported.

Computer-based simulator for catheter insertion training.

Minimally invasive surgery procedures are getting common in surgical practice; however the new interventional procedure requires different skills compared to the conventional surgical techniques. The need for training process is very important in order to successfully and safely execute a surgical procedure. Computer-based simulators, with appropriate tactile feedback device, can be an efficient method for facilitating the education and training process. In addition, virtual reality surgical simulators can reduce costs of education and provide realism with regard to tissues behaviour and real-time interaction. This work take into account the results of the HERMES Project (HEmatology Research virtual MEdical System), conceived and managed by Consorzio CETMA-Research Centre; the aim of this project is to build an integrate system in order to simulate a coronary angioplasty intervention.