5,162 research outputs found
Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks
We outline a possible theoretical framework for the quantitative modeling of
networked embodied cognitive systems. We notice that: 1) information self
structuring through sensory-motor coordination does not deterministically occur
in Rn vector space, a generic multivariable space, but in SE(3), the group
structure of the possible motions of a body in space; 2) it happens in a
stochastic open ended environment. These observations may simplify, at the
price of a certain abstraction, the modeling and the design of self
organization processes based on the maximization of some informational
measures, such as mutual information. Furthermore, by providing closed form or
computationally lighter algorithms, it may significantly reduce the
computational burden of their implementation. We propose a modeling framework
which aims to give new tools for the design of networks of new artificial self
organizing, embodied and intelligent agents and the reverse engineering of
natural ones. At this point, it represents much a theoretical conjecture and it
has still to be experimentally verified whether this model will be useful in
practice.
Computer- and robot-assisted Medical Intervention
Medical robotics includes assistive devices used by the physician in order to
make his/her diagnostic or therapeutic practices easier and more efficient.
This chapter focuses on such systems. It introduces the general field of
Computer-Assisted Medical Interventions, its aims, its different components and
describes the place of robots in that context. The evolutions in terms of
general design and control paradigms in the development of medical robots are
presented and issues specific to that application domain are discussed. A view
of existing systems, on-going developments and future trends is given. A
case-study is detailed. Other types of robotic help in the medical environment
(such as for assisting a handicapped person, for rehabilitation of a patient or
for replacement of some damaged/suppressed limbs or organs) are out of the
scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
Real-time Error Control for Surgical Simulation
Objective: To present the first real-time a posteriori error-driven adaptive
finite element approach for real-time simulation and to demonstrate the method
on a needle insertion problem. Methods: We use corotational elasticity and a
frictional needle/tissue interaction model. The problem is solved using finite
elements within SOFA. The refinement strategy relies upon a hexahedron-based
finite element method, combined with a posteriori error estimation driven local
-refinement, for simulating soft tissue deformation. Results: We control the
local and global error level in the mechanical fields (e.g. displacement or
stresses) during the simulation. We show the convergence of the algorithm on
academic examples, and demonstrate its practical usability on a percutaneous
procedure involving needle insertion in a liver. For the latter case, we
compare the force displacement curves obtained from the proposed adaptive
algorithm with that obtained from a uniform refinement approach. Conclusions:
Error control guarantees that a tolerable error level is not exceeded during
the simulations. Local mesh refinement accelerates simulations. Significance:
Our work provides a first step to discriminate between discretization error and
modeling error by providing a robust quantification of discretization error
during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM
Controlling the Error on Target Motion through Real-time Mesh Adaptation: Applications to Deep Brain Stimulation
We present an error-controlled mesh refinement procedure for needle insertion
simulation and apply it to the simulation of electrode implantation for deep
brain stimulation, including brain shift. Our approach enables to control the
error in the computation of the displacement and stress fields around the
needle tip and needle shaft by suitably refining the mesh, whilst maintaining a
coarser mesh in other parts of the domain. We demonstrate through academic and
practical examples that our approach increases the accuracy of the displacement
and stress fields around the needle without increasing the computational
expense. This enables real-time simulations. The proposed methodology has
direct implications to increase the accuracy and control the computational
expense of the simulation of percutaneous procedures such as biopsy,
brachytherapy, regional anesthesia, or cryotherapy and can be essential to the
development of robotic guidance.Comment: 21 pages, 14 figure
Enabling Motion Planning and Execution for Tasks Involving Deformation and Uncertainty
A number of outstanding problems in robotic motion and manipulation involve tasks where degrees of freedom (DoF), be they part of the robot, an object being manipulated, or the surrounding environment, cannot be accurately controlled by the actuators of the robot alone. Rather, they are also controlled by physical properties or interactions - contact, robot dynamics, actuator behavior - that are influenced by the actuators of the robot. In particular, we focus on two important areas of poorly controlled robotic manipulation: motion planning for deformable objects and in deformable environments; and manipulation with uncertainty. Many everyday tasks we wish robots to perform, such as cooking and cleaning, require the robot to manipulate deformable objects. The limitations of real robotic actuators and sensors result in uncertainty that we must address to reliably perform fine manipulation. Notably, both areas share a common principle: contact, which is usually prohibited in motion planners, is not only sometimes unavoidable, but often necessary to accurately complete the task at hand. We make four contributions that enable robot manipulation in these poorly controlled tasks: First, an efficient discretized representation of elastic deformable objects and cost function that assess a ``cost of deformation\u27 for a specific configuration of a deformable object that enables deformable object manipulation tasks to be performed without physical simulation. Second, a method using active learning and inverse-optimal control to build these discretized representations from expert demonstrations. Third, a motion planner and policy-based execution approach to manipulation with uncertainty which incorporates contact with the environment and compliance of the robot to generate motion policies which are then adapted during execution to reflect actual robot behavior. Fourth, work towards the development of an efficient path quality metric for paths executed with actuation uncertainty that can be used inside a motion planner or trajectory optimizer
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