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

Indirect Correspondence-Based Robust Extrinsic Calibration of LiDAR and Camera

LiDAR and cameras have been broadly utilized in computer vision and autonomous vehicle applications. However, in order to convert data between the local coordinate systems, we must estimate the rigid body transformation between the sensors. In this paper, we propose a robust extrinsic calibration algorithm that can be implemented easily and has small calibration error. The extrinsic calibration parameters are estimated by minimizing the distance between corresponding features projected onto the image plane. The features are edge and centerline features on a v-shaped calibration target. The proposed algorithm contributes two ways to improve the calibration accuracy. First, we use different weights to distance between a point and a line feature according to the correspondence accuracy of the features. Second, we apply a penalizing function to exclude the influence of outliers in the calibration datasets. Additionally, based on our robust calibration approach for a single LiDAR-camera pair, we introduce a joint calibration that estimates the extrinsic parameters of multiple sensors at once by minimizing one objective function with loop closing constraints. We conduct several experiments to evaluate the performance of our extrinsic calibration algorithm. The experimental results show that our calibration method has better performance than the other approaches

Multimedia System for Real-Time Photorealistic Nonground Modeling of 3D Dynamic Environment for Remote Control System

Nowadays, unmanned ground vehicles (UGVs) are widely used for many applications. UGVs have sensors including multi-channel laser sensors, two-dimensional (2D) cameras, Global Positioning System receivers, and inertial measurement units (GPS–IMU). Multi-channel laser sensors and 2D cameras are installed to collect information regarding the environment surrounding the vehicle. Moreover, the GPS–IMU system is used to determine the position, acceleration, and velocity of the vehicle. This paper proposes a fast and effective method for modeling nonground scenes using multiple types of sensor data captured through a remote-controlled robot. The multi-channel laser sensor returns a point cloud in each frame. We separated the point clouds into ground and nonground areas before modeling the three-dimensional (3D) scenes. The ground part was used to create a dynamic triangular mesh based on the height map and vehicle position. The modeling of nonground parts in dynamic environments including moving objects is more challenging than modeling of ground parts. In the first step, we applied our object segmentation algorithm to divide nonground points into separate objects. Next, an object tracking algorithm was implemented to detect dynamic objects. Subsequently, nonground objects other than large dynamic ones, such as cars, were separated into two groups: surface objects and non-surface objects. We employed colored particles to model the non-surface objects. To model the surface and large dynamic objects, we used two dynamic projection panels to generate 3D meshes. In addition, we applied two processes to optimize the modeling result. First, we removed any trace of the moving objects, and collected the points on the dynamic objects in previous frames. Next, these points were merged with the nonground points in the current frame. We also applied slide window and near point projection techniques to fill the holes in the meshes. Finally, we applied texture mapping using 2D images captured using three cameras installed in the front of the robot. The results of the experiments prove that our nonground modeling method can be used to model photorealistic and real-time 3D scenes around a remote-controlled robot

Diffusion Patterns in Convergence among High-Technology Industries: A Co-Occurrence-Based Analysis of Newspaper Article Data

Firms in high-technology industries have faced great technological and market uncertainty and volatility in the past few decades. In order to be competitive and sustainable in this environment, firms have been pursuing technological innovation, product differentiation, vertical integration, and alliances, which eventually drive industry convergence, defined as the process of blurring boundaries between previously distinct industries. Although industry convergence has greatly affected industrial structure and the economy, little research has investigated this phenomenon, especially its diffusion patterns; thus, it is still unclear which industries are converging more rapidly or have a higher potential for convergence. This paper explores these issues by investigating industry convergence in U.S. high-technology industries, using a large set of newspaper articles from 1987 to 2012. We perform a co-occurrence-based analysis to obtain information on industry convergence and estimate its diffusion patterns using an internal-influence logistic model. We find heterogeneous diffusion patterns, depending on convergent-industry pairs and their wide dispersion. In addition, we find that the potential degree of industry convergence is significantly negatively associated with its growth rate, which indicates that a great deal of time will be required for industry convergence between high-technology industries with this high potential to achieve a high degree of convergence

Self-Supervised 3D Traversability Estimation With Proxy Bank Guidance

Traversability estimation for mobile robots in off-road environments requires more than conventional semantic segmentation used in constrained environments like on-road conditions. Recently, approaches to learning a traversability estimation from past driving experiences in a self-supervised manner are arising as they can significantly reduce human labeling costs and labeling errors. However, the self-supervised data only provide supervision for the actually traversed regions, resulting in epistemic uncertainty due to the lack of knowledge on non-traversable regions, also referred to as negative data. Negative data can rarely be collected as the system can be severely damaged while logging the data. To mitigate the uncertainty in the estimation, we introduce a deep metric learning-based method to incorporate unlabeled data with a few positive and negative prototypes. Our method jointly learns binary segmentation that reduces uncertainty in addition to the regression of traversability. To firmly evaluate the proposed framework, we introduce a new evaluation metric that comprehensively evaluates the segmentation and regression. Additionally, we construct a driving dataset ‘Dtrail’ in off-road environments with a mobile robot platform, which is composed of numerous complex and diverse representations of off-road environments. We examine our method on Dtrail as well as the publicly available SemanticKITTI dataset

Microbial Diversity Responding to Changes in Depositional Conditions during the Last Glacial and Interglacial Period: NE Ulleung Basin, East Sea (Sea of Japan)

Microbial interaction with minerals are significantly linked with depositional conditions during glacial and interglacial periods, providing a unique redox condition in the sedimentary process. Abiotic geophysical and geochemical properties, including sedimentary facies, magnetic susceptibility, grain size, clay mineralogy, and distribution of elemental compositions in the sediments, have been widely used to understand paleo-depositional environments. In this study, microbial abundance and diversity in the core sediments (6.7 m long) from the northeastern slope of Dokdo Island were adapted to characterize the conventionally defined sedimentary depositional units and conditions in light of microbial habitats. The units of interglacial (Unit 1, <11.5 ka) and late glacial (Unit 2, 11.5–14.5 ka) periods in contrast to the glacial period (Unit 3, >14.5 ka) were distinctively identified in the core, showing a sharp boundary marked by the laminated Mn-carbonate (CaM) mud between bioturbated (Unit 1 and 2) and laminated mud (Unit 3). Based on the marker beds and the occurrence of sedimentary facies, core sediments were divided into three units, Unit 1 (<11.5 ka, interglacial), Unit 2 (11.5–14.5 ka, late glacial), and Unit 3 (>14.5 ka, glacial), in descending order. The sedimentation rate (0.073 cm/year), which was three times higher than the average value for the East Sea (Sea of Japan) was measured in the late glacial period (Unit 2), indicating the settlement of suspended sediments from volcanic clay in the East Sea (Sea of Japan), including Doldo Island. The Fe and Mg-rich smectite groups in Unit 2 can be transported from volcanic sediments, such as from the volcanic island in the East Sea or the east side of Korea, while the significant appearance of the Al-rich smectite group in Unit 1 was likely transported from East China by the Tsushima Warm Current (TWC). The appearance of CaM indicates a redox condition in the sedimentary process because the formation of CaM is associated with an oxidation of Mn2+ forming Mn-oxide in the ocean, and a subsequent reduction of Mn-oxide occurred, likely due to Mn-reducing bacteria resulting in the local supersaturation of Mn2+ and the precipitation of CaM. The low sea level (−120 m) in the glacial period (Unit 3) may restrict water circulation, causing anoxic conditions compared to the late glacial period (Unit 2), inducing favorable redox conditions for the formation of CaM in the boundary of the two units. Indeed, Planctomycetaceae, including anaerobic ammonium oxidation (ANAMMOX) bacteria capable of oxidizing ammonium coupled with Mn-reduction, was identified in the CaM layer by Next Generation Sequencing (NGS). Furthermore, the appearance of aerobic bacteria, such as Alphaproteobacteria, Gammaproteobacteria, and Methylophaga, tightly coupled with the abundance of phytoplankton was significantly identified in Unit 1, suggesting open marine condition in the interglacial period. Bacterial species for each unit displayed a unique grouping in the phylogenetic tree, indicating the different paleo-depositional environments favorable for the microbial habitats during the glacial and interglacial periods