Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect. https://github.com/patrykcieslak/stonefish

Autonomous motion planning is critical for efficient and safe underwater manipulation in dynamic marine environments. Current motion planning methods often fail to effectively utilize prior motion experiences and adapt to real-time uncertainties inherent in underwater settings. In this paper, we introduce an Adaptive Heuristic Motion Planner framework that integrates a Heuristic Motion Space (HMS) with Bayesian Networks to enhance motion planning for autonomous under-water manipulation. Our approach employs the Probabilistic Roadmap (PRM) algorithm within HMS to optimize paths by minimizing a composite cost function that accounts for distance, uncertainty, energy consumption, and execution time. By leveraging HMS, our framework significantly reduces the search space, thereby boosting computational performance and enabling real-time planning capabilities. Bayesian Networks are utilized to dynamically update uncertainty estimates based on real-time sensor data and environmental conditions, thereby refining the joint probability of path success. Through extensive simulations and real-world test scenarios, we showcase the advantages of our method in terms of enhanced performance and robustness. This probabilistic approach significantly advances the capability of autonomous underwater robots, ensuring optimized motion planning in the face of dynamic marine challenges.

Accurate needle insertion is an important task in many medical procedures. This paper studies the case of an autonomous needle insertion system for central venous access, which is a risky and challenging procedure involving the simultaneous manipulation of an ultrasound probe and of a catheterization needle. The goal of this medical operation is to provide access to a deep central vein, which is a key step in cardiovascular treatments or for the administration of drugs and treatments for cancer or infections. Accordingly, in this work we propose an autonomous dual-arm system for central venous access. The system is composed of two Franka robotic arms that are precisely co-registered and collaborate to achieve accurate needle insertion by combining ultrasound and bioimpedance sensing to ensure robust deep vessels visualization and venipuncture detection. The proposed system performance is evaluated on a phantom trainer through experiments simulating the jugular vein access for cardiac catheterization purposes. Quantitative results show the system is able to autonomously scan the area of interest, localize the vein and perform autonomous needle insertion with high accuracy and placement error below 1.7mm, proving the potential of the technology for real clinical use.

Augmented reality (AR) is considered one of the most promising solutions for safer procedures in several surgical specialities. Fusing patient-specific pre-operative information, typically 3D models extracted from CT scans or MRI, with real-time surgical images allows the surgeon to have detailed information on the anatomical structure of the surgical target intra-operatively. The coupling of AR and Robotics represents the next step towards introducing awareness into the surgical room, thus enhancing the surgeon's perceptual, cognitive and manipulative capabilities. This paper presents a novel integrated system for real-time AR navigation in robotic minimally invasive surgery (RMIS), composed of a robotic endoscopic camera, a robotic teleoperation implementing a software-based Remote Center of Motion (RCM), and an AR navigation software based on an initial manual registration of virtual 3D models with the real anatomy. The integrated system, as well as the individual modules, were evaluated in simulated surgical-like setups for accuracy and repeatability. The proposed system can perform high-precision tasks (position accuracy around$1 mm$and AR error lower than 7%), showing potential for application in different surgical procedures and setting the basis for autonomous robotic surgery operations.

Learning from Demonstration (LfD) is addressed in this work in order to establish a novel framework for Human-Robot Collaborative (HRC) task execution. In this context, a robotic system is trained to perform various actions by observing a human demonstrator. We formulate a latent representation of observed behaviors and associate this representation with the corresponding one for target robotic behaviors. Effectively, a mapping of observed to performed actions is defined, that abstracts action variations and differences between the human and robotic manipulators, and facilitates execution of newly-observed actions. The learned action-behaviors are then employed to accomplish task execution in an HRC scenario. Experimental results obtained regard the successful training of a robotic arm with various action behaviors and its subsequent deployment in HRC task accomplishment. The latter demonstrate the validity and efficacy of the proposed approach in human-robot collaborative setups.