Robotics and Autonomous Systems最新文献

MOVRO2: Loosely coupled monocular visual radar odometry using factor graph optimization MOVRO2：使用因数图优化的松耦合单目视觉雷达测距仪

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-25 DOI: 10.1016/j.robot.2024.104860

Vlaho-Josip Štironja , Juraj Peršić , Luka Petrović , Ivan Marković , Ivan Petrović

{"title":"MOVRO2: Loosely coupled monocular visual radar odometry using factor graph optimization","authors":"Vlaho-Josip Štironja , Juraj Peršić , Luka Petrović , Ivan Marković , Ivan Petrović","doi":"10.1016/j.robot.2024.104860","DOIUrl":"10.1016/j.robot.2024.104860","url":null,"abstract":"<div><div>Ego-motion estimation is an indispensable part of any autonomous system, especially in scenarios where wheel odometry or global pose measurement is unreliable or unavailable. In an environment where a global navigation satellite system is not available, conventional solutions for ego-motion estimation rely on the fusion of a LiDAR, a monocular camera and an inertial measurement unit (IMU), which is often plagued by drift. Therefore, complementary sensor solutions are being explored instead of relying on expensive and powerful IMUs. In this paper, we propose a method for estimating ego-motion, which we call MOVRO2, that utilizes the complementarity of radar and camera data. It is based on a loosely coupled monocular visual radar odometry approach within a factor graph optimization framework. The adoption of a loosely coupled approach is motivated by its scalability and the possibility to develop sensor models independently. To estimate the motion within the proposed framework, we fuse ego-velocity of the radar and scan-to-scan matches with the rotation obtained from consecutive camera frames and the unscaled velocity of the monocular odometry. We evaluate the performance of the proposed method on two open-source datasets and compare it to various mono-, dual- and three-sensor solutions, where our cost-effective method demonstrates performance comparable to state-of-the-art visual-inertial radar and LiDAR odometry solutions using high-performance 64-line LiDARs.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"184 ","pages":"Article 104860"},"PeriodicalIF":4.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CUAHN-VIO: Content-and-uncertainty-aware homography network for visual-inertial odometry

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-22 DOI: 10.1016/j.robot.2024.104866

Yingfu Xu, Guido C.H.E. de Croon

{"title":"CUAHN-VIO: Content-and-uncertainty-aware homography network for visual-inertial odometry","authors":"Yingfu Xu, Guido C.H.E. de Croon","doi":"10.1016/j.robot.2024.104866","DOIUrl":"10.1016/j.robot.2024.104866","url":null,"abstract":"<div><div>Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped with a downward-facing camera. The vision frontend is a content-and-uncertainty-aware homography network (CUAHN). Content awareness measures the robustness of the network toward non-homography image content, <em>e.g.</em> 3-dimensional objects lying on a planar surface. Uncertainty awareness refers that the network not only predicts the homography transformation but also estimates the prediction uncertainty. The training requires no ground truth that is often difficult to obtain. The network has good generalization that enables “plug-and-play” deployment in new environments without fine-tuning. A lightweight extended Kalman filter (EKF) serves as the VIO backend and utilizes the mean prediction and variance estimation from the network for visual measurement updates. CUAHN-VIO is evaluated on a high-speed public dataset and shows rivaling accuracy to state-of-the-art (SOTA) VIO approaches. Thanks to the robustness to motion blur, low network inference time (<span><math><mo>∼</mo></math></span>23 ms), and stable processing latency (<span><math><mo>∼</mo></math></span>26 ms), CUAHN-VIO successfully runs onboard an Nvidia Jetson TX2 embedded processor to navigate a fast autonomous MAV.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"185 ","pages":"Article 104866"},"PeriodicalIF":4.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards zero-shot cross-agent transfer learning via latent-space universal notice network 通过潜空间通用通知网络实现零距离跨代理迁移学习

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-22 DOI: 10.1016/j.robot.2024.104862

Samuel Beaussant , Sebastien Lengagne , Benoit Thuilot , Olivier Stasse

{"title":"Towards zero-shot cross-agent transfer learning via latent-space universal notice network","authors":"Samuel Beaussant , Sebastien Lengagne , Benoit Thuilot , Olivier Stasse","doi":"10.1016/j.robot.2024.104862","DOIUrl":"10.1016/j.robot.2024.104862","url":null,"abstract":"<div><div>Despite numerous improvements regarding the sample-efficiency of Reinforcement Learning (RL) methods, learning from scratch still requires millions (even dozens of millions) of interactions with the environment to converge to a high-reward policy. This is usually because the agent has no prior information about the task and its own physical embodiment. One way to address and mitigate this data-hungriness is to use Transfer Learning (TL). In this paper, we explore TL in the context of RL with the specific purpose of transferring policies from one agent to another, even in the presence of morphology discrepancies or different state–action spaces. We propose a process to leverage past knowledge from one agent (source) to speed up or even bypass the learning phase for a different agent (target) tackling the same task. Our proposed method first leverages Variational Auto-Encoders (VAE) to learn an agent-agnostic latent space from paired, time-aligned trajectories collected on a set of agents. Then, we train a policy embedded inside the created agent-invariant latent space to solve a given task, yielding a task-module reusable by any of the agents sharing this common feature space. Through several robotic tasks and heterogeneous hardware platforms, both in simulation and on physical robots, we show the benefits of our approach in terms of improved sample-efficiency. More specifically we report zero-shot generalization in some instances, where performances after transfer are recovered instantly. In worst case scenarios, performances are retrieved after fine-tuning on the target robot for a fraction of the training cost required to train a policy with similar performances from scratch.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"184 ","pages":"Article 104862"},"PeriodicalIF":4.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning temporal maps of dynamics for mobile robots 学习移动机器人的动态时序图

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-22 DOI: 10.1016/j.robot.2024.104853

Junyi Shi , Tomasz Piotr Kucner

{"title":"Learning temporal maps of dynamics for mobile robots","authors":"Junyi Shi , Tomasz Piotr Kucner","doi":"10.1016/j.robot.2024.104853","DOIUrl":"10.1016/j.robot.2024.104853","url":null,"abstract":"<div><div>Building a map representation of the surrounding environment is crucial for the successful operation of autonomous robots. While extensive research has concentrated on mapping geometric structures and static objects, the environment is also influenced by the movement of dynamic objects. Integrating information about spatial motion patterns in an environment can be beneficial for planning socially compliant trajectories, avoiding congested areas, and aligning with the general flow of people. In this paper, we introduce a deep state-space model designed to learn map representations of spatial motion patterns and their temporal changes at specific locations. Thus enabling the robot for human-compliant operation and improved trajectory forecasting in environments with evolving motion patterns. Validation of the proposed method is conducted using two datasets: one comprising generated motion patterns and the other featuring real-world pedestrian data. The model’s performance is assessed in terms of learning capability, mapping quality, and its applicability to downstream robotics tasks. For comparative assessment of mapping quality, we employ CLiFF-Map as a baseline, and CLiFF-LHMP serves as another baseline for evaluating performance in downstream motion prediction tasks. The results demonstrate that our model can effectively learn corresponding motion patterns and holds promising potential for application in robotic tasks.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"184 ","pages":"Article 104853"},"PeriodicalIF":4.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automation of polymer pressing by robotic handling with in-process parameter optimization

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-21 DOI: 10.1016/j.robot.2024.104868

Yuki Asano , Kei Okada , Shintaro Nakagawa , Naoko Yoshie , Junichiro Shiomi

{"title":"Automation of polymer pressing by robotic handling with in-process parameter optimization","authors":"Yuki Asano , Kei Okada , Shintaro Nakagawa , Naoko Yoshie , Junichiro Shiomi","doi":"10.1016/j.robot.2024.104868","DOIUrl":"10.1016/j.robot.2024.104868","url":null,"abstract":"<div><div>In this study, we introduce an autonomous system for polymer pressing that integrates robotic manipulation, specialized equipment, and machine learning optimization. This system aims to significantly reduce lead time and human labor in polymer-materials development. Our approach utilizes an arm-type robot to handle polymer beads and operate a press machine, with process parameters autonomously determined by Bayesian optimization. The keys to this automation are custom-designed press tools that are suitable for robotic handling, such as press plates or fork, a gripper—tool interface with tapered convex and concave parts that enables the handling of multiple tools by a single robot gripper, and an integrated control system that synchronizes the robot with the press machine. Additionally, we implement a closed-loop process that incorporates image processing for pressed-polymer recognition and Bayesian optimization for continuous parameter refinement, with an evaluation function that considers polymer-film thickness and press times. Verification experiments demonstrate the capability of the system to autonomously execute pressing operations and effectively propose optimized press parameters.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"185 ","pages":"Article 104868"},"PeriodicalIF":4.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Delta- and Kalman-filter designs for multi-sensor pose estimation on spherical mobile mapping systems 用于球形移动测绘系统多传感器姿态估计的德尔塔滤波器和卡尔曼滤波器设计

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-21 DOI: 10.1016/j.robot.2024.104852

Fabian Arzberger , Tim Schubert , Fabian Wiecha , Jasper Zevering , Julian Rothe , Dorit Borrmann , Sergio Montenegro , Andreas Nüchter

{"title":"Delta- and Kalman-filter designs for multi-sensor pose estimation on spherical mobile mapping systems","authors":"Fabian Arzberger , Tim Schubert , Fabian Wiecha , Jasper Zevering , Julian Rothe , Dorit Borrmann , Sergio Montenegro , Andreas Nüchter","doi":"10.1016/j.robot.2024.104852","DOIUrl":"10.1016/j.robot.2024.104852","url":null,"abstract":"<div><div>Spherical mobile mapping systems are not thoroughly studied in terms of inertial pose estimation filtering. The underlying inherent rolling motion introduces high angular velocities and aggressive system dynamics around all principal axes. This motion profile also needs different modeling compared to state-of-the-art competitors, which heavily focus on more rotationally-restricted systems such as UAV, handheld, or cars. In this work we compare our previously proposed “Delta-filter”, which was heavily motivated by the sensors inability to provide covariance estimations, with a Kalman-filter design using a covariance model. Both filters fuse two 6-DoF pose estimators with a motion model in real-time, however the designs are theoretically suitable for an arbitrary number of estimators. We evaluate the trajectories against ground truth pose measurement from an OptiTrack™ motion capturing system. Furthermore, as our spherical systems are equipped with laser-scanners, we evaluate the resulting point clouds against ground truth maps available from a Riegl VZ400 terrestrial laser-scanner (TLS). Our source code and datasets can be found on github (Arzberger, 2023).</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"184 ","pages":"Article 104852"},"PeriodicalIF":4.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe tracking control for free-flying space robots via control barrier functions 通过控制屏障功能实现自由飞行太空机器人的安全跟踪控制

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-21 DOI: 10.1016/j.robot.2024.104865

Chengrui Shi , Tao Meng , Kun Wang , Jiakun Lei , Weijia Wang , Renhao Mao

{"title":"Safe tracking control for free-flying space robots via control barrier functions","authors":"Chengrui Shi , Tao Meng , Kun Wang , Jiakun Lei , Weijia Wang , Renhao Mao","doi":"10.1016/j.robot.2024.104865","DOIUrl":"10.1016/j.robot.2024.104865","url":null,"abstract":"<div><div>Safety is a critical problem for space robots in future complex autonomous On-Orbit Services. In this paper, we propose a real-time and guaranteed method for whole-body safe tracking control of free-flying space robots using High Order Control Barrier Functions (HOCBFs).</div><div>We start by utilizing capsule-shaped safety envelopes for an accurate approximation of space robots. This is followed by the development of HOCBF-based safety filters to ensure simultaneous collision avoidance and compliance with specified joint limits. To mitigate feasibility issues, we incorporate the optimal decay method into our safety filter design. Furthermore, we introduce a data-driven re-planning mechanism to avoid local minimums of control barrier functions. Such a mechanism primarily operates through anomaly detection of tracking behavior using One-Class Support Vector Machines.</div><div>Numerical experiments demonstrate that our method effectively ensures safety of space robots under complicated circumstances without compromising the system’s ability to achieve its intended goals.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"184 ","pages":"Article 104865"},"PeriodicalIF":4.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hierarchical simulation-based push planner for autonomous recovery in navigation blocked scenarios of mobile robots 基于分层模拟的推送规划器，用于移动机器人导航受阻情况下的自主恢复

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-20 DOI: 10.1016/j.robot.2024.104867

Alessio De Luca , Luca Muratore , Nikos Tsagarakis

{"title":"A hierarchical simulation-based push planner for autonomous recovery in navigation blocked scenarios of mobile robots","authors":"Alessio De Luca , Luca Muratore , Nikos Tsagarakis","doi":"10.1016/j.robot.2024.104867","DOIUrl":"10.1016/j.robot.2024.104867","url":null,"abstract":"<div><div>Mobile robotic platforms that are expected to be engaged in applications domains characterized by unstructured terrains and environment settings will unavoidably face mobility constraints that may not be overcome by classical navigation planning and obstacle avoidance/negotiation tools. Endowing these robots with additional skills, which enable them to interact and manipulate obstacles blocking their pathway, will significantly enhance their ability to deal with such conditions, permitting them to perform their mission more robustly when encountering such unstructured and cluttered scenes. This paper proposes a novel hierarchical simulation-based push planner framework that searches for a sequence of pushing actions to move obstacles toward a planned goal position. This aims at overcoming obstacle challenges that block the navigation of the robot toward a target location and, therefore, can lead to the failure of the navigation plan and the overall mission of the robot. The planned pushing actions enable the robot to relocate objects in the scene avoiding obstacles and considering environmental constraints identified by an elevation or an occupancy map. The online simulations of the pushing actions are carried out by exploiting the Mujoco physics engine. The framework was validated in the Gazebo simulation environment and in real platforms such as the hybrid wheeled-legged robot CENTAURO and the mobile cobot RELAX.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"184 ","pages":"Article 104867"},"PeriodicalIF":4.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GPC-LIVO: Point-wise LiDAR-inertial-visual odometry with geometric and photometric composite measurement model

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-20 DOI: 10.1016/j.robot.2024.104864

Chenxi Ye, Bingfei Nan

{"title":"GPC-LIVO: Point-wise LiDAR-inertial-visual odometry with geometric and photometric composite measurement model","authors":"Chenxi Ye, Bingfei Nan","doi":"10.1016/j.robot.2024.104864","DOIUrl":"10.1016/j.robot.2024.104864","url":null,"abstract":"<div><div>In the pursuit of precision within Simultaneous Localization and Mapping (SLAM), multi-sensor fusion emerges as a validated strategy with vast potential in robotics applications. This work presents GPC-LIVO, an accurate and robust LiDAR-Inertial-Visual Odometry system that integrates geometric and photometric information into one composite measurement model with point-wise updating architecture. GPC-LIVO constructs a belief factor model to assign different weights on geometric and photometric observations in the measurement model and adopts an adaptive error-state Kalman filter state estimation back-end to dynamically estimate the covariance of two observations. Since LiDAR points have larger measurement errors at endpoints and edges, we only fuse photometric information for LiDAR planar features and propose a corresponding validation method based on the associated image plane. Comprehensive experimentation is conducted on GPC-LIVO, encompassing both publicly available data sequences and data collected from our bespoke hardware setup. The results conclusively establish the better performance of our proposed system compare to other state-of-art odometry frameworks, and demonstrate its ability to operate effectively in various challenging environmental conditions. GPC-LIVO outputs states estimation at a high frequency(1-5 kHz, varying based on the processed LiDAR points in a frame) and achieves comparable time consumption for real-time running.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"185 ","pages":"Article 104864"},"PeriodicalIF":4.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ontological framework for high-level task replanning for autonomous robotic systems 自主机器人系统高级别任务重新规划的本体论框架

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-19 DOI: 10.1016/j.robot.2024.104861

Rodrigo Bernardo , João M.C. Sousa , Paulo J.S. Gonçalves

引用次数: 0