Kaiserslautern - Fachbereich Informatik
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We study high dimensional integration in the quantum model of computation. We develop quantum algorithms for integration of functions from Sobolev classes \(W^r_p [0,1]^d\) and analyze their convergence rates. We also prove lower bounds which show that the proposed algorithms are, in many cases, optimal within the setting of quantum computing. This extends recent results of Novak on integration of functions from Hölder classes.
Interactive graphics has been limited to simple direct illumination that commonly results in an artificial appearance. A more realistic appearance by simulating global illumination effects has been too costly to compute at interactive rates. In this paper we describe a new Monte Carlo-based global illumination algorithm. It achieves performance of up to 10 frames per second while arbitrary changes to the scene may be applied interactively. The performance is obtained through the effective use of a fast, distributed ray-tracing engine as well as a new interleaved sampling technique for parallel Monte Carlo simulation. A new filtering step in combination with correlated sampling avoids the disturbing noise artifacts common to Monte Carlo methods.
We present an algorithm for determining quadrature rules for computing the direct illumination of predominantly diffuse objects by high dynamic range images. The new method precisely reproduces fine shadow detail, is much more efficient as compared to Monte Carlo integration, and does not require any manual intervention.
Many rendering problems can only be solved using Monte Carlo integration. The noise and variance inherent with the statistical method efficiently can be reduced by stratification. So far only uncorrelated stratification methods were used that in addition depend on the dimension of the integration domain. Based on rank-1-lattices we present a new stratification technique that removes this dependency on dimension, is much more efficient by correlation, is trivial to implement, and robust to use. The superiority of the new scheme is demonstrated for standard rendering algorithms.
Monte Carlo & Beyond
(2002)
Image synthesis often requires the Monte Carlo estimation of integrals. Based on a generalized concept of stratification we present an efficient sampling scheme that consistently outperforms previous techniques. This is achieved by assembling sampling patterns that are stratified in the sense of jittered sampling and N-rooks sampling at the same time. The faster convergence and improved anti-aliasing are demonstrated by numerical experiments.
This paper analyzes the problem of sensor-based colli-sion detection for an industrial robotic manipulator. A method to perform collision tests based on images taken from several stationary cameras in the work cell is pre-sented. The collision test works entirely based on the im-ages, and does not construct a representation of the Carte-sian space. It is shown how to perform a collision test for all possible robot configurations using only a single set of images taken simultaneously.
Manipulating Deformable Linear Objects: Manipulation Skill for Active Damping of Oscillations
(2002)
While handling deformable linear objects (DLOs), such as hoses, wires or leaf springs, with an industrial robot at high speed, unintended and undesired oscillations that delay further operations may occur. This paper analyzes oscillations based on a simple model with one degree of freedom (DOF) and presents a method for active open-loop damping. Different ways to interpret an oscillating DLO as a system with 1 DOF lead to translational and rotational adjustment motions. Both were implemented as a manipulation skill with a sepa-rate program that can be executed immediately after any robot motion. We showed how these manipulation skills can generate the needed adjustment motions automatically based on the readings of a wrist-mounted force/torque sensor. Experiments demonstrated the effectiveness under various conditions.
Virtual Robot Programming for Deformable Linear Objects: System concept and Prototype Implementation
(2002)
In this paper we present a method and system for robot programming using virtual reality techniques. The proposed method allows intuitive teaching of a manipulation task with haptic feedback in a graphical simulation system. Based on earlier work, our system allows even an operator who lacks specialized knowledge of robotics to automatically generate a robust sensor-based robot program that is ready to execute on different robots, merely by demonstrating the task in virtual reality.
It is difficult for robots to handle a vibrating deformable object. Even for human beings it is a high-risk operation to, for example, insert a vibrating linear object into a small hole. However, fast manipulation using a robot arm is not just a dream; it may be achieved if some important features of the vibration are detected online. In this paper, we present an approach for fast manipulation using a force/torque sensor mounted on the robot's wrist. Template matching method is employed to recognize the vibrational phase of the deformable objects. Therefore, a fast manipulation can be performed with a high success rate, even if there is acute vibration. Experiments inserting a deformable object into a hole are conducted to test the presented method. Results demonstrate that the presented sensor-based online fast manipulation is feasible.