Robotic minimally invasive surgery has been a subject of intense research and development over the last three decades, due to the clinical advantages it holds for patients and doctors alike. Particularly for drug delivery mechanisms, higher precision and the ability to follow complex trajectories in three dimensions (3D), has led to interest in flexible, steerable needles such as the programmable bevel-tip needle (PBN). Steering in 3D, however, holds practical challenges for surgeons, as interfaces are traditionally designed for straight line paths. This work presents a pilot study undertaken to evaluate a novel human-machine visual interface for the steering of a robotic PBN, where both qualitative evaluation of the interface and quantitative evaluation of the performance of the subjects in following a 3D path are measured. A series of needle insertions are performed in phantom tissue (gelatin) by the experiment subjects. User could adequately use the system with little training and low workload, and reach the target point at the end of the path with millimeter range accuracy.

In this paper, an automatic planner for minimally invasive neurosurgery is presented. The solution provides the neurosurgeon with the best path to connect a user-defined entry point with a target in accordance with a specific cost function. The approach guarantees the avoidance of obstacles which can be found along the insertion pathway. The method is tailored to the EDEN2020* programmable bevel-tip needle, a multisegment steerable probe intended to be used to perform drug delivery for the treatment of glioblastomas. A sample-based heuristic search inspired by the BIT* algorithm is used to define the asymptotically-optimal solution in terms of path length, followed by a smoothing phase to meet the required kinematic constraints of the needle. To account for inaccuracies in catheter modeling, which could determine unexpected control errors over the insertion procedure, an uncertainty margin is defined in order to increase the algorithm's safety. The feasibility of the proposed solution was demonstrated by testing the method in simulated neurosurgical scenarios with different degrees of obstacle occupancy and against other sample-based algorithms present in literature: RRT, RRT* and an enhanced version of the RRT-Connect.

This article reports on experiments about haptic perception aimed at measuring the force/torque differential thresholds applied to the hand-arm system. The experimental work analyzes how force is sent back to the user by means of a 6 degrees-of-freedom haptic device. Our findings on force perception indicate that the just-noticeable-difference is generally higher than previously reported in the literature and not constant along the stimulus continuum. We found evidence that the thresholds change also among the different directions. Furthermore, asymmetries in force perceptions, which were not described in previous reports, can be evinced for most of the directions. These findings support our claim that human beings perceive forces differently along different directions, thus suggesting that perception can also be enhanced by suitable signal processing, that is, with a manipulation of the force signal before it reaches the haptic device. We think that the improvement of the user perception can have a great impact in many applications and in particular we are focusing on surgical teleoperation scenarios.

In this paper a force scaling function for an haptic system is the output of the psychophysics experiments that have been carried out with the aim of better understanding the human perception capabilities. The experimental work consists in measuring the differential thresholds of force perception applied to the hand-arm system. These findings support our claim that the human perception of forces and torques depends on force intensity and works differently along different directions, thus suggesting that perception can be enhanced by suitable scaling. We have identified a scaling function for each direction and we have shown that this variable scalings can be safely embedded in a passivity based teleoperation system in order to improve the feeling perceived by the user during the interaction with remote environments.

The two main categories of deformable models used in surgical simulators are Mass Spring Models (MSM) and Finite Element Models (FEM). Mass spring models are often preferred due to their simplicity and low computational cost and because they allow to perform topology changes on the modeled body without significant computational overhead. The principal drawback of the mass spring model is the need of complex calibration procedure since they don't have a clear physical meaning. In this paper we propose a new method to calibrate mass spring models. Our method uses CAT data to identify mass values and deformation measures to define elastic coefficient and damping ratio for the springs of the model. Spring parameters are obtained through a genetic algorithm that minimizes the difference between the model and the measured behavior. The algorithm we developed to compute the masses was tested with medical CAT data whereas the spring algorithm correctness was tested with synthetic models. Simulation verifications are presented.

In the last few years, robotics education has been proposed in Universities and High Schools, to take advantage of the appeal of this discipline to teach scientific subjects. Robotics education is greatly improved when classroom teaching is supported by adequate laboratory courses and experiments following the "learning by doing" paradigm. However, cost of robotic laboratory equipment is an important issue, and several low cost instruments have been developed to permit an effective teaching in robotics at an affordable cost. These learning tools have demonstrated the importance and effectiveness of hand-on experiences and of group work in robotics learning. Among the best known and most used tools for robotics teaching, there is the Legocopy Mindstormtrade set, which is normally used to develop and program mobile robots, often biologically inspired and cooperating. In this paper however, we describe our experience with using LEGO kits in the development of teaching curricula for fixed robot manipulators. We found that the LEGO set is also an excellent tool to analyze robot kinematics and trajectory planning, and we describe our experience with a laboratory course designed to address kinematic properties of fixed robots

Teleoperation with force feedback is a complex task and a good haptic device is a key element. In this paper we present an innovative hardware/software structure used to control an actuated 6 dof joystick in a teleoperation task. To increase speed and reliability, parts of the kinematic calculation are embedded into the joystick controller. To implement this idea we used an FPGA to handle both the low level tasks and the algorithmic part of the approach. This speedup does not preclude the possibility of changing the control strategies, the parameters and the feedback calculations as well, necessary to carry out experiments about human perception. Thus the need of flexibility and performance, and the choice of an FPGA. We tested the setup proposed in a real teleoperation task within our software framework (Penelope) collecting data of speed, precision and reliability

In this paper we report on the efforts carried out at the Robotics Laboratory ALTAIR of the University of Verona (Italy) towards the development of a high performance architecture for bilateral, i.e. force reflecting, teleoperation system. This architecture, called Penelope, takes into account the main features of a haptics system: real-time behavior, distributed resources, general purpose structure and safe exchange of data. The emphasis here is on heterogeneous hardware and software within the same structure