This paper presents a real-time control framework for a snake robot with hyper-kinematic redundancy under dynamic active constraints for minimally invasive surgery. A proximity query (PQ) formulation is proposed to compute the deviation of the robot motion from predefined anatomical constraints. The proposed method is generic and can be applied to any snake robot represented as a set of control vertices. The proposed PQ formulation is implemented on a graphic processing unit, allowing for fast updates over 1 kHz. We also demonstrate that the robot joint space can be characterized into lower dimensional space for smooth articulation. A novel motion parameterization scheme in polar coordinates is proposed to describe the transition of motion, thus allowing for direct manual control of the robot using standard interface devices with limited degrees of freedom. Under the proposed framework, the correct alignment between the visual and motor axes is ensured, and haptic guidance is provided to prevent excessive force applied to the tissue by the robot body. A resistance force is further incorporated to enhance smooth pursuit movement matched to the dynamic response and actuation limit of the robot. To demonstrate the practical value of the proposed platform with enhanced ergonomic control, detailed quantitative performance evaluation was conducted on a group of subjects performing simulated intraluminal and intracavity endoscopic tasks.
This paper describes a novel hardware accelerator for Monte Carlo (MC) simulation, and illustrates its implementation in field programmable gate array (FPGA) technology for speeding up financial applications. Our accelerator is based on a generic architecture, which combines speed and flexibility by integrating a pipelined MC core with an on-chip instruction processor. We develop a generic number system representation for determining the choice of number representation that meets numerical precision requirements. Our approach is then used in a complex financial engineering application, involving the Brace, Gatarek and Musiela (BGM) interest rate model for pricing derivatives. We address, in our BGM model, several challenges including the generation of Gaussian distributed random numbers and pipelining of the MC simulation. Our BGM application, based on an off-the-shelf system with a Xilinx XC2VP30 device at 50 MHz, is over 25 times faster than software running on a 1.5 GHz Intel Pentium machine.
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