Journal of Field Robotics最新文献

{"title":"Motion planning and contact force distribution for heavy-duty hexapod robots walking on unknown rugged terrains","authors":"Liang Ding,&nbsp;Xiao Gong,&nbsp;Lei Hu,&nbsp;Guanyu Wang,&nbsp;Zhongxi Shao,&nbsp;Huaiguang Yang,&nbsp;Haibo Gao,&nbsp;Zongquan Deng","doi":"10.1002/rob.22338","DOIUrl":"10.1002/rob.22338","url":null,"abstract":"<p>Heavy-duty hexapod robots have impressive stability, high load-bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming-based Contact Force Distribution (DQP-based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1680-1701"},"PeriodicalIF":4.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670600","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}

{"title":"Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation","authors":"Yuchun Wang,&nbsp;Cheng Gong,&nbsp;Jianwei Gong,&nbsp;Peng Jia","doi":"10.1002/rob.22345","DOIUrl":"10.1002/rob.22345","url":null,"abstract":"<p>Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multilayer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network-long short-term memory network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1702-1723"},"PeriodicalIF":4.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670109","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}

{"title":"Autonomous underwater vehicle link alignment control in unknown environments using reinforcement learning","authors":"Yang Weng,&nbsp;Sehwa Chun,&nbsp;Masaki Ohashi,&nbsp;Takumi Matsuda,&nbsp;Yuki Sekimori,&nbsp;Joni Pajarinen,&nbsp;Jan Peters,&nbsp;Toshihiro Maki","doi":"10.1002/rob.22348","DOIUrl":"10.1002/rob.22348","url":null,"abstract":"<p>High-speed underwater wireless optical communication holds immense promise in ocean monitoring and surveys, providing crucial support for the real-time sharing of observational data collected by autonomous underwater vehicles (AUVs). However, due to inaccurate target information and external interference in unknown environments, link alignment is challenging and needs to be addressed. In response to these challenges, we propose a reinforcement learning-based alignment method to control the AUV to establish an optical link and maintain alignment. Our alignment control system utilizes a combination of sensors, including a depth sensor, Doppler velocity log (DVL), gyroscope, ultra-short baseline device, and acoustic modem. These sensors are used in conjunction with a particle filter to observe the environment and estimate the AUV's state accurately. The soft actor-critic algorithm is used to train a reinforcement learning-based controller in a simulated environment to reduce pointing errors and energy consumption in alignment. After experimental validation in simulation, we deployed the controller on an actual AUV called Tri-TON. In experiments at sea, Tri-TON maintained the link and angular pointing errors within 1 m and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mn>1</mn>\u0000 \u0000 <msup>\u0000 <mn>0</mn>\u0000 \u0000 <mo>∘</mo>\u0000 </msup>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> $1{0}^{circ }$</annotation>\u0000 </semantics></math>, respectively. Experimental results demonstrate that the proposed alignment control method can establish underwater optical communication between AUV fleets, thus improving the efficiency of marine surveys.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1724-1743"},"PeriodicalIF":4.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670085","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}

{"title":"Comparative study of soil interaction and driving characteristics of different agricultural and space robots in an agricultural environment","authors":"Malte Wirkus,&nbsp;Stefan Hinck,&nbsp;Christian Backe,&nbsp;Jonathan Babel,&nbsp;Vadim Riedel,&nbsp;Nele Reichert,&nbsp;Andrej Kolesnikov,&nbsp;Tobias Stark,&nbsp;Jens Hilljegerdes,&nbsp;Hilmi Doğu Küçüker,&nbsp;Arno Ruckelshausen,&nbsp;Frank Kirchner","doi":"10.1002/rob.22347","DOIUrl":"10.1002/rob.22347","url":null,"abstract":"<p>This paper investigates four different mobile robots with respect to their driving characteristics and soil preservation properties in an agricultural environment. Thereby, robots of classical design from agriculture as well as systems from space robotics with advanced locomotion concepts are considered to determine the individual advantages of each rover concept with respect to the application domain. Locomotion experiments were conducted to analyze the general driving behavior, tensile force, and obstacle-surmounting capability and ground interaction of each robot. Various soil conditions typical for the area of application are taken into account, which are varied in terms of moisture and density. The presented work covers the specification of the conducted experiments, documentation of the implementation as well as analysis and evaluation of the collected data. In the evaluation, particular attention is paid to the change in driving characteristics under different soil conditions, as well as to the soil stress caused by driving, since soil quality is of critical importance for agricultural applications. The analysis shows that the advanced locomotion concepts, as used in space robotics, also have positive implications for certain requirements in agricultural applications, such as maneuverability in wet conditions and soil conservation. The results show potential for design innovations in agricultural robotics that can be used, to open up new fields of application for instance in the context of precision farming.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"2009-2042"},"PeriodicalIF":4.2,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140629009","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}

{"title":"PAW: Prediction of wildlife animals using a robot under adverse weather conditions","authors":"Parminder Kaur,&nbsp;Sachin Kansal,&nbsp;V. P. Singh","doi":"10.1002/rob.22344","DOIUrl":"10.1002/rob.22344","url":null,"abstract":"<p>Image dehazing and object detection are two different research areas that play a vital role in machine learning. When merged together and implemented in real-time, it is a boon in the field of artificial intelligence, specifically robotics. Object detection and tracking are two of the major implementations in almost the entire robot's training and learning. The learning of the robot depends on the images; these images can be camera-captured images or a pretrained data set. Real-time outdoor images clicked in bad weather conditions, such as mist, haze, smog, and fog, often suffer from poor visibility, and the consequences are incorrect results and hence an unexpected robot's behavior. To overcome these consequences, we have presented a novel approach to object detection and identification during adverse weather conditions. This method is proposed to be implemented in a real-time environment to monitor animal behavior near railway tracks during fog, haze, and smog. This is not limited to specific application areas but can be used to identify endangered species and take active steps to save them from mishap. The deployment is done in a real-time indoor environment using Tortoisebot mobile robot with a robot operating system framework.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2506-2517"},"PeriodicalIF":4.2,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140628881","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}

{"title":"Water entry locomotion strategy for a stranding bionic robotic fish","authors":"Huijie Dong,&nbsp;Zhipeng Ji,&nbsp;Yan Meng,&nbsp;Di Chen,&nbsp;Tiezhu Qiao,&nbsp;Junzhi Yu","doi":"10.1002/rob.22352","DOIUrl":"10.1002/rob.22352","url":null,"abstract":"<p>Similar to other underwater robots, bionic robotic fish face entrapment risks when stranded due to wave action or water level drop. In this paper, we propose a locomotion strategy for a whale shark-inspired robotic fish, enabling it to autonomously return to aquatic environments after being stranded on land. This strategy is informed by the terrestrial locomotion capabilities of mudskippers and is particularly significant given the considerable mass of such robotic fish, which compounds the difficulty of land-based movement. First, we introduce a lightweight YOLOv5 model-based algorithm for deep-water area recognition, which identifies the direction for the bionic robot fish to re-enter the water. Subsequently, pectoral fin-based crawling gaits are designed by the innate two degrees of freedom within the existing pectoral fin structure of the robot. These gaits empower the robotic fish to move on a multitude of terrestrial terrains. Extended field experiments have validated the effectiveness of our water recognition algorithm and locomotion strategy, confirming the ability of the whale shark-inspired robotic fish to perform successful water entry maneuvers from the shore. Additionally, the capability to traverse various landforms are also verified. This work provides valuable insights into self-rescue mechanisms for stranding underwater robots and promotes practical applications of bionic robotics.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 8","pages":"2493-2505"},"PeriodicalIF":4.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630726","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}

{"title":"Data-driven iterative learning cooperative trajectory tracking control for multiple autonomous underwater vehicles with input saturation constraints","authors":"Chengxi Wu,&nbsp;Hamid Reza Karimi,&nbsp;Liang Shan,&nbsp;Yuewei Dai","doi":"10.1002/rob.22343","DOIUrl":"10.1002/rob.22343","url":null,"abstract":"<p>This paper investigates the cooperative trajectory tracking (CTT) control problem of multiple autonomous underwater vehicles (AUVs). The multi-AUV system is characterized by uncertain dynamics, being subjected to the impact about input saturation constraints and unmeasurable disturbances. First, a neural network-based data-driven control algorithm is proposed for the multi-AUV system with unmeasurable disturbances and model parameters uncertain. The radial basis function neural network is employed to estimate the primary pseudo parameters of an equivalent data model, established through dynamic linearization methods. Subsequently, an iterative learning control approach based on adaptive gain is designed to act as a feedforward scheme along the iteration axis to enhance the tracking accuracy within a time constraint. Third, to prove that the resulting CTT control system fulfills the bounded stability under the proposed control approach, a formal stability analysis is provided. Finally, a simulation case study is conducted to illustrate the effectiveness of the proposed CTT control approach.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 7","pages":"2475-2487"},"PeriodicalIF":4.2,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578620","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}

{"title":"The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on-orbit close proximity operations","authors":"Bryce Doerr,&nbsp;Keenan Albee,&nbsp;Monica Ekal,&nbsp;Rodrigo Ventura,&nbsp;Richard Linares","doi":"10.1002/rob.22308","DOIUrl":"10.1002/rob.22308","url":null,"abstract":"<p>On-orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on-orbit assembly, repair, and astronaut assistance. Of these scenarios, on-orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on-orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free-flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on-orbit assembly and other scenarios require collision-avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long-horizon planning, accomplished using offline and online sampling-based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information-aware planning framework for on-orbit reconfiguration model learning; and (3) connection with robust control tools to provide low-level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on-orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free-flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free-flyers will need to consider.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 6","pages":"1645-1679"},"PeriodicalIF":4.2,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140587601","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}

{"title":"LTA-OM: Long-term association LiDAR–IMU odometry and mapping","authors":"Zuhao Zou,&nbsp;Chongjian Yuan,&nbsp;Wei Xu,&nbsp;Haotian Li,&nbsp;Shunbo Zhou,&nbsp;Kaiwen Xue,&nbsp;Fu Zhang","doi":"10.1002/rob.22337","DOIUrl":"10.1002/rob.22337","url":null,"abstract":"<p>This paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA-OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR-inertial odometry (FAST-LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA-OM is implemented to be functionally complete, including loop detection and correction, false-positive loop closure rejection, long-term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real-time LTA mapping, which exploits the direct scan-to-map registration of FAST-LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift-free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life-long mapping. Furthermore, LTA-OM has valuable features for robot control and path planning, including high-frequency and real-time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA-OM is versatile as it is applicable to both multiline spinning and solid-state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA-OM and other state-of-the-art LiDAR systems with 18 data sequences. The results show that LTA-OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA-OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 7","pages":"2455-2474"},"PeriodicalIF":4.2,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602368","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}

{"title":"Acoustic tracking of moving marine targets using a single autonomous surface receiver","authors":"Ilan Git,&nbsp;Matan Samina,&nbsp;Shachar Givon,&nbsp;Ronen Segev,&nbsp;Moshe Kiflawi,&nbsp;Ohad Ben-Shahar","doi":"10.1002/rob.22335","DOIUrl":"10.1002/rob.22335","url":null,"abstract":"<p>While marine animal behavior is often studied in constrained lab setups, a more reliable exploration should be done in their natural environment and without human interference. This task becomes excessively more challenging when quantitative data are needed in large and unconstrained aquatic environments. Toward that end, researchers widely use acoustic positioning telemetry to remotely track their subjects, though this often requires an extensive network of receivers placed in the environment ahead of time. This study proposes a new tracking method that continuously tracks and reports the trajectory of a target in unconstrained marine environments using a <i>single-moving</i> acoustic receiver. Instead of deploying an extensive array of static receivers, we use a single receiver mounted on an autonomous surface vehicle to obtain highly accurate results with much cheaper and simpler means. The receiver position and earlier target location estimations are used to calculate an optimal trajectory for the receiver, which in turn provides subsequent readings and target localizations based on a new variant of the Time Difference of Arrival approach. We demonstrate the performance of the proposed methods using both simulations and field experiments.</p>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"41 5","pages":"1498-1520"},"PeriodicalIF":4.2,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140587645","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}