Smooth Model Predictive Path Integral Control without Smoothing

We present a sampling-based control approach that can generate smooth actions for general nonlinear systems without external smoothing algorithms. Model Predictive Path Integral (MPPI) control has been utilized in numerous robotic applications due to its appealing characteristics to solve non-convex optimization problems. However, the stochastic nature of sampling-based methods can cause significant chattering in the resulting commands. Chattering becomes more prominent in cases where the environment changes rapidly, possibly even causing the MPPI to diverge. To address this issue, we propose a method that seamlessly combines MPPI with an input-lifting strategy. In addition, we introduce a new action cost to smooth control sequence during trajectory rollouts while preserving the information theoretic interpretation of MPPI, which was derived from non-affine dynamics. We validate our method in two nonlinear control tasks with neural network dynamics: a pendulum swing-up task and a challenging autonomous driving task. The experimental results demonstrate that our method outperforms the MPPI baselines with additionally applied smoothing algorithms.Comment: Accepted to IEEE Robotics and Automation Letters (and IROS 2022). Our video can be found at https://youtu.be/ibIks6ExGw

Trajectory Optimization and Robust Tracking Control for Off-Road Autonomous Vehicle

This paper presents a control strategy for real-time trajectory optimization and robust path tracking for unmanned off-road vehicles to ensure both stability and performance. The approach takes advantage of a two-degree-of-freedom control framework that combines predictive driving control through perceptual information and feedback control for robust stability. Trajectory generation leverages model predictive control where the particle swarm optimization is used as an optimizer to address problems of the non-smoothness of the traversability information and nonlinear nature of vehicle dynamics. By using the exteroceptive perception, the vehicle could estimate traversability and adapt its motion to achieve fast and smooth driving. For the feedback controller, a system level synthesis is used to faithfully track the planned path despite uncertainty and unknown disturbance. Specifically, we focus on realizing the proposed method as a practical means for the real-time control system. The effectiveness of this method is validated through extensive numerical simulations and experimental tests, demonstrating its practical applicability in uncertain environments for autonomous vehicle navigation

Sleep Quality and Attention May Correlate With Hand Grip Strength: FARM Study

Objective To determine the socio-demographic, psychologic, hematologic, or other relevant factors associated with hand grip strength in Korean farmers. Methods A total of 528 healthy Korean farmers were enrolled. Hand grip strength was measured in both hands using a hydraulic dynamometer. Socio-demographic characteristics were assessed and anthropometric measurements were obtained. Psycho-cognitive measurements such as sleep quality (Pittsburgh Sleep Quality Index) and Go/No-Go test response time were conducted. In addition to physical measurements, serologic parameters including insulin-like growth factor 1 were measured. The factors associated with hand grip strength were analyzed using multiple linear regression analysis after adjusting for age, height, and weight. Results The mean hand grip strength was associated with the Pittsburgh Sleep Quality Index total score (β=-0.12, p=0.01), the Go/No-Go test response time (β=-0.18, p=0.001), vitamin D (β=0.12, p=0.02), and insulin-like growth factor 1 levels (β=0.1, p=0.045). In female farmers, hand grip strength was only associated with the Pittsburgh Sleep Quality Index total score (β=-0.32, p<0.001). Conclusion The results of this study demonstrate that hand grip strength was associated with sleep quality and attention in Korean farmers

Novel STI Technology for Enhancing Reliability of High-k/Metal Gate DRAM

The challenges associated with semiconductor are increasing because of the rapid changes in the semiconductor market and the extreme scaling of semiconductors, with some processes reaching their technological limits. In the case of gate dielectrics, these limitations can be overcome by adopting high-k metal gate (HKMG) architecture instead of the previously used poly silicon/silicon oxy-nitride (PSION) structure. However, implementing the HKMG in a conventional DRAM process degrades the gate oxide. Therefore, in this study, a shallow trench isolation (STI) technology was developed to improve the gate oxide reliability in gate first HKMG DRAM structures. A novel STI process was developed to prevent the reduction in the oxide growth that occurs when the STI seam (or void) generated during the STI gap fill process meets the low temperature gate oxide process of the HKMG with SiGe. With the spacer STI (S-STI) structure, the ALD spacer was formed in the STI space region before the STI gap fill process to control the position of the STI seam (or void). Thus, a favorable environment for the growth of the gate oxide was established under the reduced effect of STI seam, and the oxide reliability was improved while maintaining the original structure and processes of the HKMG DRAM. Various analyses confirmed that the reliability was enhanced without the inherent characteristics of the HKMG being affected. These results revealed that the STI integration technology introduced herein improves the oxide reliability of HKMG DRAM products and maintains their technological excellence for the various complex needs of a rapidly changing market