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Reconfigurable Inflated Soft Arms

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Reconfigurable Inflated Soft Arms

IEEE International Conference on robotics and Automation (ICRA)

作者： Nam Gyun Kim Jee-Hwan Ryu Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Inflatable structures have attracted considerable research attention in many fields owing to their numerous advantages, such as being light and able to engage in interactions safely. However, in most cases, the inflatable structure can only have one stable configuration, which is undesirable for robotic arms. This study proposes a novel inflatable structure that can be easily reconfigured into multiple stable configurations, even with single-body inflation. In the proposed mechanism, the structure length can be freely adjusted, and its respective joints can be set in the desired directions to facilitate the reconfiguration of its pose. An additional advantage of the proposed mechanism is that it can withstand external forces as well as its own weight. This study analyzes and experimentally validates the shape locking and load-carrying properties of the proposed mechanism. Further, the fabrication process and design guidelines for the proposed mechanism are presented. Through a suitable demonstration, the proposed mechanism is shown to exhibit multiple stable configurations and lock its poses.

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K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

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arXiv 2022年

作者： Paek, Dong-Hee Kong, Seung-Hyun Wijaya, Kevin Tirta CCS Graduate School of Mobility Korea Republic of Robotics Program KAIST Korea Republic of

Unlike RGB cameras that use visible light bands (384∼769 THz) and Lidars that use infrared bands (361∼331 THz), Radars use relatively longer wavelength radio bands (77∼81 GHz), resulting in robust measurements in adverse weathers. Unfortunately, existing Radar datasets only contain a relatively small number of samples compared to the existing camera and Lidar datasets. This may hinder the development of sophisticated data-driven deep learning techniques for Radar-based perception. Moreover, most of the existing Radar datasets only provide 3D Radar tensor (3DRT) data that contain power measurements along the Doppler, range, and azimuth dimensions. As there is no elevation information, it is challenging to estimate the 3D bounding box of an object from 3DRT. In this work, we introduce kaist-Radar (K-Radar), a novel large-scale object detection dataset and benchmark that contains 35K frames of 4D Radar tensor (4DRT) data with power measurements along the Doppler, range, azimuth, and elevation dimensions, together with carefully annotated 3D bounding box labels of objects on the roads. K-Radar includes challenging driving conditions such as adverse weathers (fog, rain, and snow) on various road structures (urban, suburban roads, alleyways, and highways). In addition to the 4DRT, we provide auxiliary measurements from carefully calibrated high-resolution Lidars, surround stereo cameras, and RTK-GPS. We also provide 4DRT-based object detection baseline neural networks (baseline NNs) and show that the height information is crucial for 3D object detection. And by comparing the baseline NN with a similarly-structured Lidar-based neural network, we demonstrate that 4D Radar is a more robust sensor for adverse weather conditions. All codes are available at https://***/kaist-avelab/k-radar. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

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arXiv 2022年

作者： Lee, Daegyu Seong, Hyunki Kang, Gyuree Han, Seungil Shim, D. Hyunchul Yoon, Yoonjin Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of Civil and Environmental Engineering KAIST Daejeon Korea Republic of

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment. © 2022, CC BY-SA.

关键词： Autonomous vehicles

Enhancing Contact Stability in Admittance-Type Haptic Interaction Using Bidirectional Time-Domain Passivity Control

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Enhancing Contact Stability in Admittance-Type Haptic Intera...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Seong-Su Park Huseyin Tugcan Dinc Kwang-Hyun Lee Jee-Hwan Ryu Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

The present paper proposes a novel strategy to enhance admittance-type haptic interaction using bidirectional time-domain passivity control. While admittance-type haptic interaction is widely employed in human-robot collaboration, its ability to render low virtual inertia can be limited, leading to unstable interactions with rigid environments. The proposed strategy seeks to stabilize a lower range of virtual inertia while maintaining responsive behavior in free space. The Franka Emika Collaborative robot was utilized in various experiments to test the approach, and the results indicate that the bidirectional time-domain passivity controller can improve interaction performance relative to the conventional unidirectional time-domain passivity approach. This technique may aid in reducing operator fatigue during the manipulation of heavy, non-back drivable industrial robots while preserving a lightweight feel.

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High-Curvature Consecutive Tip Steering of a Soft Growing Robot for Improved Target Reachability

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High-Curvature Consecutive Tip Steering of a Soft Growing Ro...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dong-Geol Lee Nam Gyun Kim Jee-Hwan Ryu Robotics Program Korea Advanced Institute of Science and Tech-nology (KAIST) Daejeon Republic of Korea Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Over the years, soft growing robots that allow the feeding of new materials at their tips have attracted considerable attention owing to their unique locomotion characteristics. However, accessing targets over highly curved passages by steering compliant continuum bodies remains challenging. To this end, this study proposes a new tip steering mechanism that imparts soft growing robots with consecutive high-curvature steering ability. We place a hyper-redundant rolling contact joint and twisted string actuator (TSA) at the tip of a soft growing robot to provide high-curvature continuous steering with a smaller scale factor, allowing consecutive turns. The proposed small form factor continuous curvature tip steering mechanism allows the steering mechanism to stay at the tip during steering, improving accessibility by enabling consecutive turns. The improved accessibility of the proposed mechanism is demonstrated based on better confined space reachability compared with that of conventional whole-body steering soft growing robots, along with a consecutive high-curvature pipe navigation demonstration.

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Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction

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arXiv 2023年

作者： Choi, Hwan-Soo Jeong, Jongoh Cho, Young Hoo Yoon, Kuk-Jin Kim, Jong-Hwan Robot Intelligence Technology Lab School of Engineering KAIST Daejeon Korea Republic of dSPECTER Seoul Korea Republic of Visual Intelligence Lab Robotics Program Dept. of Mechanical Engineering KAIST Daejeon Korea Republic of

Sensor fusion approaches for intelligent self-driving agents remain key to driving scene understanding given visual global contexts acquired from input sensors. Specifically, for the local waypoint prediction task, single-modality networks are still limited by strong dependency on the sensitivity of the input sensor, and thus recent works therefore promote the use of multiple sensors in fusion in feature level in practice. While it is well known that multiple data modalities encourage mutual contextual exchange, it requires global 3D scene understanding in real-time with minimal computation upon deployment to practical driving scenarios, thereby placing greater significance on the training strategy given a limited number of practically usable sensors. In this light, we exploit carefully selected auxiliary tasks that are highly correlated with the target task of interest (e.g., traffic light recognition and semantic segmentation) by fusing auxiliary task features and also using auxiliary heads for waypoint prediction based on imitation learning. Our RGB-LIDAR-based multi-task feature fusion network, coined Cognitive TransFuser, augments and exceeds the baseline network by a significant margin for safer and more complete road navigation in the CARLA simulator. We validate the proposed network on the Town05 Short and Town05 Long Benchmark through extensive experiments, achieving up to 44.2 FPS real-time inference time. © 2023, CC BY.

关键词： Semantics

Goal-Conditioned Reinforcement Learning Approach for Autonomous Parking in Complex Environments

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Goal-Conditioned Reinforcement Learning Approach for Autonom...

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Taeyoung Kim Taemin Kang Seungah Son Kuk Won Ko Dongsoo Har CCS Graduate School of Mobility Korea Advanced Institute of Science and Technology(KAIST) Daejeon Rep. of Korea The Robotics Program Korea Advanced Institute of Science and Technology(KAIST) Daejeon Rep. of Korea Department of Future Mobility Engineering Halla University Wonju Rep. of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

In complex urban environments, autonomous vehicle parking is challenged by irregularly parked cars, static obstacles, and narrow spaces. Traditional parking methods are often limited to well-structured parking lots and fail to adapt to unstructured scenarios like alleyways with obstacles and randomly positioned vehicles. This paper addresses this limitation by applying goal-conditioned reinforcement learning to enable autonomous vehicles to park in congested environments based on specified target positions and orientations. A custom simulation environment is developed using Pygame to simulate parking scenarios across four progressive levels of difficulty, with obstacles to test adaptability. Three RL algorithms, SAC, HER, and an improved HER variant, are implemented to compare the performance in the simulation environment. Experimental results demonstrate that the proposed approach significantly improves parking success rates and trajectory efficiency in complex scenarios, contributing to robust, adaptable parking solutions for autonomous vehicles in real-world applications.

关键词： Training Space vehicles Three-dimensional displays Urban areas Reinforcement learning Trajectory Reliability Vehicle dynamics Autonomous vehicles Testing

Quatro++: Robust Global Registration Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM

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arXiv 2023年

作者： Lim, Hyungtae Kim, Beomsoo Kim, Daebeom Lee, Eungchang Mason Myung, Hyun School of Electrical Engineering KAIST [Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Artificial Intelligence Hanyang University Seoul04763 Korea Republic of Robotics Program at KAIST Daejeon34141 Korea Republic of

Global registration is a fundamental task that estimates the relative pose between two viewpoints of 3D point clouds. However, there are two issues that degrade the performance of global registration in LiDAR SLAM: one is the sparsity issue and the other is degeneracy. The sparsity issue is caused by the sparse characteristics of the 3D point cloud measurements in a mechanically spinning LiDAR sensor. The degeneracy issue sometimes occurs because the outlier-rejection methods reject too many correspondences, leaving less than three inliers. These two issues have become more severe as the pose discrepancy between the two viewpoints of 3D point clouds becomes greater. To tackle these problems, we propose a robust global registration framework, called Quatro++. Extending our previous work that solely focused on the global registration itself, we address the robust global registration in terms of the loop closing in LiDAR SLAM. To this end, ground segmentation is exploited to achieve robust global registration. Through the experiments, we demonstrate that our proposed method shows a higher success rate than the state-of-the-art global registration methods, overcoming the sparsity and degeneracy issues. In addition, we show that ground segmentation significantly helps to increase the success rate for the ground vehicles. Finally, we apply our proposed method to the loop closing module in LiDAR SLAM and confirm that the quality of the loop constraints is improved, showing more precise mapping results. Therefore, the experimental evidence corroborated the suitability of our method as an initial alignment in the loop closing. Our code is available at https://***. © 2023, CC BY-NC-ND.

关键词： Ground vehicles

Is a Simulation better than Teleoperation for Acquiring Human Manipulation Skill Data?

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Is a Simulation better than Teleoperation for Acquiring Huma...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Donghyeon Kim Seong-Su Park Kwang-Hyun Lee Dongheui Lee Jee-Hwan Ryu Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Applied Science Research Institute Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Autonomous Systems Technische Universitat Wien (TU Wien) Vienna Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study explores the feasibility of using simulations as a better interface to collect human object manipulation skills for learning from demonstrations (LfD). Recently, numerous researchers have started introducing teleoperation systems to acquire human manipulation skills. However, capturing the subtle, force-involved interaction skills of humans in teleoperation is still challenging due to its inherent dynamic delays and feedback transparency. This research evaluates the effectiveness of demonstration data obtained through simulation versus teleoperation. To evaluate the efficacy of this approach, tasks such as plane cutting, tight peg-in-hole, and deformable pipe plugging were performed to assess the quality of demonstrations acquired. The experimental results highlight the effectiveness of demonstration through simulation in capturing the operator’s force-involved interaction skills. Simulation creates an environment similar to performing tasks with bare hands by minimising dynamic delays due to the exclusion of physical robots and effectively rendering high stiffness. As a result, the demonstration through simulation method has proven effective in extracting interaction data and capturing physical task performance skills.

关键词： Hands Dynamics Rendering (computer graphics) Data models Delays Data mining Intelligent robots