| Summary: | Simulation has been widely used for training and rehearsing difficult or unusual
actions in several fields such as aviation and the military. However, the simulators
available in some disciplines do not fulfil the requirements of reliability and
accuracy that users demand. This happens in neurosurgery. In order to overcome
these difficulties, this thesis presents a multimodal Neurosurgery Simulator
focused on patient-specific surgical learning and training.
One of the aspects that most influences the behavioural reality of a simulator
is the way in which the scene objects interfere. For that reason, detecting collisions
and giving them a feasible response is particularly important. This work presents
the collision handling methods for rigid and deformable volumetric objects and
their haptic response to be integrated into the Neurosurgery Simulator. With the
aim of evaluating our methods in terms of continuity and stability, the present
document analyses the time consumption of the collision handling algorithms and
the stability of the force parameters they return.
Real-time virtual reality simulators require accuracy but are also time
dependent. Thus, their computational cost is a vital aspect. This thesis also
proposes a methodology to optimize the time consumption of collision detection
algorithms that are based on the uniform spatial partition technique. It is validated
experimentally and compared to other approaches. Additionally, the optimization
is applied to our deformable collision detection method in order to improve its
performance.
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