Improving Rigid 3-D Calibration for Robotic Surgery

Autonomy is the next frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this article, we propose a novel calibration technique for a surgical scenario with a da Vinci Research Kit (dVRK) robot. Camera and robotic arms calibration are necessary to precise position and emulate expert surgeon. The novel calibration technique is tailored for RGB-D cameras. Different tests performed on relevant use cases prove that we significantly improve precision and accuracy with respect to state of the art solutions for similar devices on a surgical-size setups. Moreover, our calibration method can be easily extended to standard surgical endoscope used in real surgical scenario

A Passivity-Based Bilateral Teleoperation Architecture using Distributed Nonlinear Model Predictive Control

Bilateral teleoperation systems allow the telepresence of an operator while working remotely. Such ability becomes crucial when dealing with critical environments like space, nuclear plants, rescue, and surgery. The main properties of a teleoperation system are the stability and the transparency which, in general, are in contrast and they cannot be fully achieved at the same time. In this paper, we will present a novel model predictive controller that implements a passivity-based bilateral teleoperation algorithm. Our solution mitigatesthe chattering issue arising when resorting to the energy tank(or reservoir) mechanism by forcing the passivity as a hard constraint on the system evolution

Human-Robot Interfaces in Autonomous Surgical Robots

This chapter addresses a very complex problem that can be expressed in a very simple form: is it possible to automate surgery? We believe that, if a solution can ever be approached, it would lie in the intersection of robotics, automation, and cognition, since factors such as experience, knowledge, and intuition are as important as mechanism design and control algorithms in successful surgery. The feasibility of a solution to this problem was analyzed during the European project Intelligent Robotic Surgery (I-SUR), a project of the Seventh Framework Programme during the period 2011 to 2014. In this project, we developed general methods for cognitive surgical robots capable of combining sensing, dexterity, and cognitive capabilities to carry out autonomously simple surgical actions, such as puncturing and suturing. To narrow further the scope of this research, we addressed the well-known surgical area of kidney interventions, whose variability and uncertainty are limited, but still representative of general situations and requiring strong cognitive capabilities to ensure the correct execution of the surgery. We examined the reasoning aspects of these domains, the control capabilities needed to carry out these surgical actions, and we integrated them into a robotic test bed performing autonomous surgical actions on anatomically correct phantoms

Mixed control: the discrete-time case

A stochastic H 1e and a mixed stochastic H2/H 1e control problem for discrete-time systems are considered and solved. Conditions for existence of a solution are derived, based on the solvability of an equivalent minimax problem. In this framework, it is possible to consider at the same time both stochastic and deterministic disturbances highlighting their mutual e4ects. The controller can be designed by solving a system of three coupled Riccati equations. Such equations display how the H2-type minimization problem and the H 1e-type constraint influence each other

Mixed H2/Hinfty Control: The Discrete-Time Case

A stochastic H 1e and a mixed, stochastic, H2 /H 1e control problem for discrete-time systems are considered and solved. Conditions for existence of a solution are derived, based on the solvability of an equivalent minimax problem. In this framework, it is possible to consider at the same time both stochastic and deterministic disturbances highlighting their mutual effects. The controller can be designed by solving a system of three coupled Riccati equations. Such equations display how the H2-type minimization problem and the H 1e - type constraint influence each other

Improving the performance of a Laser Guide Star Adaptive Optics system using identification methods

The light coming from a star travels undisturbed for billions of kilometers until it reaches the boundaries of the Earth atmosphere: there small differences in the refraction index distort the spherical wavefront, creating phase errors in the image-forming ray paths. Even at the best sites, ground-based telescopes observing at visible wavelengths cannot, because of atmospheric turbulence alone, achieve an angular resolution better than telescopes of 10- to 20-cm diameter. Adaptive Optics (AO) is a technique to remove the effects of these distortions in real-time by operating a deformable mirror that rapidly adapts its shape to the atmospheric disturbance canceling it. ESO (European Southern Observatory) now operates several AO systems in the Paranal observatoryon the chilean Andes, where the 4 telescopes of the VLT (Very Large Telescope) are installed. Several of them are the result of a common project called MACAO, for Multi Application Curvature Adaptive Optics. In this paper we will focus on one particular installation, SINFONI, that combines MACAO with an integral field spectrometer. MACAO for SINFONI features a quite complex control loop because the guide star used here is artificial, generated with a laser. One subsystem of MACAO, theLGS tip/tilt control, does not perform as expected. We analyse the behaviour of this subsystem by first identifying its model and then we try to improve it by designing an optimal controller

Energy-Efficient Intrusion Detection and Mitigation for Networked Control Systems Security

This paper proposes an energy-efficient security-aware architecture for wireless control systems to be used in factory automation. We face deception attacks which corrupt commands and measurements in a smart way and with intermittent behavior to produce the highest damage without being discovered. The intrusion is hard to be distinguished from normal disturbance. Furthermore, protection against attacks is energy consuming and it would be desirable to activate protection only when needed. We propose packet-based selective encryption to reduce energy consumption and to detect when an attack starts and ends. Since energy consumption depends also on packet transmission rate, especially during attacks, we also propose to adapt it according to instantaneous control performance

Compensating non-linear effects in a MIMO system with unobservable and uncontrollable modes

In this paper we propose an architecture to deal with non-linear effects like saturation that can severly limit the performance of an adaptive optics (AO) system while preserving good performance in both point-spread function (PSF) output and piston management. An AO system uses a wave-front sensor as input device and a deformable mirror (DM) as output device. Since both the input and output devices have several degrees of freedom, this is a MIMO system. We consider the case of MACAO (for Multi-Application Curvature Adaptive Optics) which has a wave-front sensor with 60 channels and a DM with 60 actuators

Model predictive control over delay-based differentiated services control networks

Networked control systems are a well-known sub-set of cyber-physical systems in which the plant is controlled by sending commands through a digital packet-based network. Current control networks provide advanced channel access mechanisms to guarantee low delay on a limited fraction of packets (low-delay class) while the other packets (un-protected class) experience a higher delay which increases with channel utilization. We investigate the extension of model predictive control to choose both the command value and its assignment to one of the two classes according to the predicted state of the plant and the knowledge of network condition. Experimental results show that more commands are assigned to the low-delay class when either the tracking error is high or the network condition is bad

Statistical Methods for Estimating the Dynamical Parameters of Manipulators

The determination of the dynamical parameters of robot manipulators is crucial in many applications where model based control architectures are needed to match stringent performance requirements. Unfortunately only the kinematic model of the robot is usually available whereas the dynamical parameters are unknown and very difficult to compute by first principle or CAD analysis. In the last decades several algorithms have been proposed to identify these parameters mainly based on the least-square analysis. In this paper we present an identification method based on a statistical algorithm never used so far in robotics, which brings new insight into the understanding of the identified parameters and improves robustness of the computation. We think that this approach represents a significant improvement as compared to using standard statistical tools, as shown by results of the identification of the Puma 200 robot