CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods

Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made available an increasing amount of data that enable data-driven Causal Discovery (CD). Considering the growing number of recently proposed CD methods, it is necessary to introduce strict benchmarking procedures on publicly available datasets since they represent the foundation for a fair comparison and validation of different methods. This work introduces two novel public datasets for CD in continuous manufacturing processes. The first dataset employs the well-known Tennessee Eastman simulator for fault detection and process control. The second dataset is extracted from an ultra-processed food manufacturing plant, and it includes a description of the plant, as well as multiple ground truths. These datasets are used to propose a benchmarking procedure based on different metrics and evaluated on a wide selection of CD algorithms. This work allows testing CD methods in realistic conditions enabling the selection of the most suitable method for specific target applications. The datasets are available at the following link: https://github.com/giovanniMenComment: Supplementary Materials at: https://github.com/giovanniMen/CPCaD-Benc

cost effective quality assessment in industrial parts manufacturing via optical acquisition

Abstract We tackle the problem of dimensional verification via optical acquisition systems in the context of industrial manufacturing processes. Optical methods for quality inspection play a crucial part in the transition process to industry 4.0 and, despite the lack of international standardization, several solutions are available to industries that need to provide dimensional verification to their customers. Unfortunately most of these solutions are still economically unavailable to the majority of small or medium companies. In this paper we present an optical system based on low-cost components and we demonstrate that it provides useful and reliable information in quality inspection procedures

Automatic detection of procedural knowledge in robotic-assisted surgical texts

Purpose The automatic extraction of knowledge about intervention execution from surgical manuals would be of the utmost importance to develop expert surgical systems and assistants. In this work we assess the feasibility of automatically identifying the sentences of a surgical intervention text containing procedural information, a subtask of the broader goal of extracting intervention workflows from surgical manuals. Methods We frame the problem as a binary classification task. We first introduce a new public dataset of 1958 sentences from robotic surgery texts, manually annotated as procedural or non-procedural. We then apply different classification methods, from classical machine learning algorithms, to more recent neural-network approaches and classification methods exploiting transformers (e.g., BERT, ClinicalBERT). We also analyze the benefits of applying balancing techniques to the dataset. Results The architectures based on neural-networks fed with FastText’s embeddings and the one based on ClinicalBERT outperform all the tested methods, empirically confirming the feasibility of the task. Adopting balancing techniques does not lead to substantial improvements in classification. Conclusion This is the first work experimenting with machine / deep learning algorithms for automatically identifying procedural sentences in surgical texts. It also introduces the first public dataset that can be used for benchmarking different classification methods for the task

Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty

Autonomous robotic surgery requires deliberation, i.e. the ability to plan and execute a task adapting to uncertain and dynamic environments. Uncertainty in the surgical domain is mainly related to the partial pre-operative knowledge about patient-specific anatomical properties. In this paper, we introduce a logic-based framework for surgical tasks with deliberative functions of monitoring and learning. The DEliberative Framework for Robot-Assisted Surgery (DEFRAS) estimates a pre-operative patient-specific plan, and executes it while continuously measuring the applied force obtained from a biomechanical pre-operative model. Monitoring module compares this model with the actual situation reconstructed from sensors. In case of significant mismatch, the learning module is invoked to update the model, thus improving the estimate of the exerted force. DEFRAS is validated both in simulated and real environment with da Vinci Research Kit executing soft tissue retraction. Compared with state-of-the-art related works, the success rate of the task is improved while minimizing the interaction with the tissue to prevent unintentional damage

Inductive learning of surgical task knowledge from intra-operative expert feedback

Knowledge-based and particularly logic-based systems for task planning and execution guarantee trustability and safety of robotic systems interacting with humans. However, domain knowledge is usually incomplete. This paper proposes a novel framework for task knowledge refinement from real-time user feedback, based on inductive logic programming

Autonomous tissue retraction with a biomechanically informed logic based framework

Autonomy in robot-assisted surgery is essential to reduce surgeons\u2019 cognitive load and eventually improve the overall surgical outcome. A key requirement for autonomy in a safety-critical scenario as surgery lies in the generation of interpretable plans that rely on expert knowledge. Moreover, the Autonomous Robotic Surgical System (ARSS) must be able to reason on the dynamic and unpredictable anatomical environment, and quickly adapt the surgical plan in case of unexpected situations. In this paper, we present a modular Framework for Robot-Assisted Surgery (FRAS) in deformable anatomical environments. Our framework integrates a logic module for task-level interpretable reasoning, a biomechanical simulation that complements data from real sensors, and a situation awareness module for context interpretation. The framework performance is evaluated on simulated soft tissue retraction, a common surgical task to remove the tissue hiding a region of interest. Results show that the framework has the adaptability required to successfully accomplish the task, handling dynamic environmental conditions and possible failures, while guaranteeing the computational efficiency required in a real surgical scenario. The framework is made publicly available

automatic process modeling with time delay neural network based on low level data

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Biomechanical modelling of probe to tissue interaction during ultrasound scanning

Purpose: Biomechanical simulation of anatomical deformations caused by ultrasound probe pressure is of outstanding importance for several applications, from the testing of robotic acquisition systems to multi-modal image fusion and development of ultrasound training platforms. Different approaches can be exploited for modelling the probe-tissue interaction, each achieving different trade-offs among accuracy, computation time and stability. Methods: We assess the performances of different strategies based on the finite element method for modelling the interaction between the rigid probe and soft tissues. Probe\u2013tissue contact is modelled using (i) penalty forces, (ii) constraint forces, and (iii) by prescribing the displacement of the mesh surface nodes. These methods are tested in the challenging context of ultrasound scanning of the breast, an organ undergoing large nonlinear deformations during the procedure. Results: The obtained results are evaluated against those of a non-physically based method. While all methods achieve similar accuracy, performance in terms of stability and speed shows high variability, especially for those methods modelling the contacts explicitly. Overall, prescribing surface displacements is the approach with best performances, but it requires prior knowledge of the contact area and probe trajectory. Conclusions: In this work, we present different strategies for modelling probe\u2013tissue interaction, each able to achieve different compromises among accuracy, speed and stability. The choice of the preferred approach highly depends on the requirements of the specific clinical application. Since the presented methodologies can be applied to describe general tool\u2013tissue interactions, this work can be seen as a reference for researchers seeking the most appropriate strategy to model anatomical deformation induced by the interaction with medical tools

UnityFlexML: Training Reinforcement Learning Agents in a Simulated Surgical Environment

Sim-to-real Deep Reinforcement Learning (DRL) has shown promising in subtasks automation for surgical robotic systems, since it allows to safely perform all the trial and error attempts needed to learn the optimal control policy. However, a realistic simulation environment is essential to guarantee direct transfer of the learnt policy from the simulated to the real system. In this work, we introduce UnityFlexML, an open-source framework providing support for soft bodies simulation and state-of-the-art DRL methods. We demonstrate that a DRL agent can be successfully trained within UnityFlexML to manipulate deformable fat tissues for tumor exposure during a nephrectomy procedure. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the DRL agent. The proposed framework represents an essential component for the development of autonomous robotic systems, where the interaction with the deformable anatomical environment is involved

Dynamic movement primitives: volumetric obstacle avoidance

Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation