Deformations are an essential aspect of our interaction with real bodies, prompting the development of many modelling and simulation methods for virtual environments. Some of the methods address specific classes of interaction, e.g. pushing, grabbing, cutting or needle insertion, whereas hybrid approaches have been proposed to deal with more complex scenarios. However, a general strategy to combine different simulation methods is still not available. This paper presents a unified approach to combine different methods, each optimised for a specific interaction and object type. Our approach is tailored to the needs of simulating haptic Human-Robot Interactions and allows abstracting from the implementation details of different methods for modelling deformable objects. It has been integrated with collision detection and friction models, ported to a graphic processing unit (GPU), and demonstrated with realistic simulations and experiments.

Abstract Following the continuous increase in computational power of consumer hardware, interactive virtual environments have been recently enriched with more and more complex deformable objects. However, many physics engines are still very limited in the way they handle interacting rigid and deformable objects. This paper proposes a constraint‐based approach to real‐time simulation of coupled rigid and deformable objects capable of providing two‐way interactions. Similar techniques have seen widespread usage for either rigid or deformable objects, but not for the simultaneous simulation of both. By extending such approaches, we show not only how interaction is possible but also how it can be performed at real‐time rates. We address contact response and also show how to implement typical constraints to enforce limitations in the degrees of freedom and to enhance the dynamical properties of deformable objects. The method is easily integrated into existing physics engines that use similar constraint solvers and is independent on the kind of deformable object paradigm chosen. The provided simulation results show that the method is fast and effective in handling contacts between rigid and deformable objects and in simulating friction and other kinds of constraints. Copyright © 2015 John Wiley & Sons, Ltd.

This paper addresses the problem of training of operators for telemanipulation tasks. In particular, it describes the development of a physics based virtual environment that allows a user to train in the control of an innovative robotic tool designed for the retrieval of spent nuclear fuels.

Interactive simulations of deformable bodies are a growing research area with possible applications in several fields, i.e. computer aided surgery. The main implementation issue is to mimic the real behavior of the body at the extremely high rates required by haptic devices. Since even high-end computers have inadequate performance, one possible solution is to exploit the parallelism of modern Graphics Processing Units. In this paper we present our research aiming at moving the whole computational process from theCPUto theGPUtaking advantage of the computational power of the graphics hardware. We use a mass-spring model, augmented with local damping coefficients and volume preservation forces. Collision detection is performed against external rigid bodies with high complexity mesh, such as the skeletonpsilas one. The user interacts with the model by controlling virtual tools, i.e. probes or tweezers. Haptic forces are computed onGPUand the results are asyncronously transferred to theCPU. Our approach can simulate the deformation of complex models with gravity and interaction with environment and tools at a frame rate higher than 1 KHz, making it suitable for visual rendering and haptic feedback.

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.