3 research outputs found
Tractable robot simulation for terrain leveling
This thesis describes the problem of terrain leveling, in which one or more robots or
vehicles are used to
atten a terrain. The leveling operation is carried out either in
preparation for construction, or for terrain reparation. In order to develop and prototype
such a system, the use of simulation is advantageous. Such a simulation requires
high fidelity to accurately model earth moving robots, which navigate uneven terrain
and potentially manipulate the terrain itself. It has been found that existing tools
for robot simulation typically do not adequately model deformable and/or uneven
terrain. Software which does exist for this purpose, based on a traditional physics
engine, is difficult if not impossible to run in real-time while achieving the desired
accuracy. A number of possible approaches are proposed for a terrain leveling system
using autonomous mobile robots. In order to test these approaches in simulation, a
2D simulator called Alexi has been developed, which uses the predictions of a neural
network rather than physics simulation, to predict the motion of a vehicle and changes
to a terrain. The neural network is trained using data captured from a high-fidelity
non-real-time 3D simulator called Sandbox. Using a trained neural network to drive
the 2D simulation provides considerable speed-up over the high-fidelity 3D simulation,
allowing behaviour to be simulated in real-time while still capturing the physics of
the agents and the environment. Two methods of simulating terrain in Sandbox are
explored with results related to performance given for each. Two variants of Alexi
are also explored, with results related to neural network training and generalization
provided
FEA-based simulation of breast deformation in real-time using artificial neural network
Treatment of breast cancer involves two stages: diagnosis and treatment. It is difficult to correlate the imaging results at the two stages because as the patient’s posture changes during treatment, the images captured during diagnosis do not represent the tumor location during the treatment. In the absence of real-time imaging during treatment, the visualization of tumor location is challenging for surgeons.
There are many challenges for breast deformation simulation. For example, material properties are very important to simulate the deformation accurately. The simulation speed will decide whether the technology is applicable for clinical use. But because of the limit of hardware, achieving real time simulation is difficult.
This thesis focuses on investigating visualization of breast deformation for different patient’s positions. We utilized magnetic resonance imaging (MRI) of a patient collected during diagnosis for this study. This data was preprocessed to form a 3D reconstructed model that was used to run a finite element analysis (FEA) simulation. FEA simulates the deformation of breast tissues for different constraints, such as glandular ratio and gravity angle. However, FEA simulation of such deformation can take a few minutes to as much as 40 minutes to complete using a 8 cores computer. To obtain real-time visualization, we constructed a neural network (NN) model that takes breast gravity angle and glandular / fat ratio (breast material) as input to estimate breast deformation for different patient’s positions offline. This NN is used to predict the deformation of the breast and provide visualization in real-time (5 ms prediction time).
To further validate our result, we carried out MRI of a breast phantom in several angles (to mimic various patient postures). We also implemented an iterative technique to estimate material properties. This data was used to simulate breast deformations at different posture angles. A similar approach was implemented to build an NN model. Our results show that NN has the ability to map the gravity direction to the breast shape and tumor location accurately, while, keeping run time to a minimum
Real-time simulation of soft tissue deformation for surgical simulation
Surgical simulation plays an important role in the training, planning and evaluation of many surgical procedures. It requires realistic and real-time simulation of soft tissue deformation under interaction with surgical tools. However, it is challenging to satisfy both of these conflicting requirements. On one hand, biological soft tissues are complex in terms of material compositions, structural formations, and mechanical behaviours, resulting in nonlinear deformation characteristics under an external load. Due to the involvement of both material and geometric nonlinearities, the use of nonlinear elasticity causes a highly expensive computational load, leading to the difficulty to achieve the real-time computational performance required by surgical simulation. On the other hand, in order to satisfy the real-time computational requirement, most of the existing methods are mainly based on linear elasticity under the assumptions of small deformation and homogeneity to describe deformation of soft tissues. Such simplifications allow reduced runtime computation; however, they are inadequate for modelling nonlinear material properties such as anisotropy, heterogeneity and large deformation of soft tissues. In general, the two conflicting requirements of surgical simulation raise immense complexity in modelling of soft tissue deformation. This thesis focuses on establishment of new methodologies for modelling of soft tissue deformation for surgical simulation. Due to geometric and material nonlinearities in soft tissue deformation, the existing methods have only limited capabilities in achieving nonlinear soft tissue deformation in real-time. In this thesis, the main focus is devoted to the real-time and realistic modelling of nonlinear soft tissue deformation for surgical simulation. New methodologies, namely new ChainMail algorithms, energy propagation method, and energy balance method, are proposed to address soft tissue deformation. Results demonstrate that the proposed methods can simulate the typical soft tissue mechanical properties, accommodate isotropic and homogeneous, anisotropic and heterogeneous materials, handle incompressibility and viscoelastic behaviours, conserve system energy, and achieve realistic, real-time and stable deformation. In the future, it is projected to extend the proposed methodologies to handle surgical operations, such as cutting, joining and suturing, for topology changes occurred in surgical simulation