Autonomous Robots最新文献

{"title":"Multi-robot geometric task-and-motion planning for collaborative manipulation tasks","authors":"Hejia Zhang,&nbsp;Shao-Hung Chan,&nbsp;Jie Zhong,&nbsp;Jiaoyang Li,&nbsp;Peter Kolapo,&nbsp;Sven Koenig,&nbsp;Zach Agioutantis,&nbsp;Steven Schafrik,&nbsp;Stefanos Nikolaidis","doi":"10.1007/s10514-023-10148-y","DOIUrl":"10.1007/s10514-023-10148-y","url":null,"abstract":"<div><p>We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in <i>synchronous</i>, <i>monotone</i> setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. We focus on collaborative manipulation tasks where the robots have to adopt intelligent collaboration strategies to be successful and effective, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging MR-GTAMP domains and show that it outperforms two state-of-the-art baselines with respect to the planning time, the resulting plan length and the number of objects moved. We also show that our framework can be applied to underground mining operations where a robotic arm needs to coordinate with an autonomous roof bolter. We demonstrate plan execution in two roof-bolting scenarios both in simulation and on robots.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1537 - 1558"},"PeriodicalIF":3.5,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10148-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136022819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Unsupervised dissimilarity-based fault detection method for autonomous mobile robots","authors":"Mahmut Kasap,&nbsp;Metin Yılmaz,&nbsp;Eyüp Çinar,&nbsp;Ahmet Yazıcı","doi":"10.1007/s10514-023-10144-2","DOIUrl":"10.1007/s10514-023-10144-2","url":null,"abstract":"<div><p>Autonomous robots are one of the critical components in modern manufacturing systems. For this reason, the uninterrupted operation of robots in manufacturing is important for the sustainability of autonomy. Detecting possible fault symptoms that may cause failures within a work environment will help to eliminate interrupted operations. When supervised learning methods are considered, obtaining and storing labeled, historical training data in a manufacturing environment with faults is a challenging task. In addition, sensors in mobile devices such as robots are exposed to different noisy external conditions in production environments affecting data labels and fault mapping. Furthermore, relying on a single sensor data for fault detection often causes false alarms for equipment monitoring. Our study takes requirements into consideration and proposes a new unsupervised machine-learning algorithm to detect possible operational faults encountered by autonomous mobile robots. The method suggests using an ensemble of multi-sensor information fusion at the decision level by voting to enhance decision reliability. The proposed technique relies on dissimilarity-based sensor data segmentation with an adaptive threshold control. It has been tested experimentally on an autonomous mobile robot. The experimental results show that the proposed method is effective for detecting operational anomalies. Furthermore, the proposed voting mechanism is also capable of eliminating false positives in case of a single source of information is utilized.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1503 - 1518"},"PeriodicalIF":3.5,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136232753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Chasing millimeters: design, navigation and state estimation for precise in-flight marking on ceilings","authors":"Christian Lanegger,&nbsp;Michael Pantic,&nbsp;Rik Bähnemann,&nbsp;Roland Siegwart,&nbsp;Lionel Ott","doi":"10.1007/s10514-023-10141-5","DOIUrl":"10.1007/s10514-023-10141-5","url":null,"abstract":"<div><p>Precise markings for drilling and assembly are crucial, laborious construction tasks. Aerial robots with suitable end-effectors are capable of markings at the millimeter scale. However, so far, they have only been demonstrated under laboratory conditions where rigid state estimation and navigation assumptions do not impede robustness and accuracy. This paper presents a complete aerial layouting system capable of precise markings on-site under realistic conditions. We use a compliant actuated end-effector on an omnidirectional flying base. Combining a two-stage factor-graph state estimator with a Riemannian Motion Policy-based navigation stack, we avoid the need for a globally consistent state estimate and increase robustness. The policy-based navigation is structured into individual behaviors in different state spaces. Through a comprehensive study, we show that the system creates highly precise markings at a relative precision of 1.5 mm and a global accuracy of 5–6 mm and discuss the results in the context of future construction robotics.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1405 - 1418"},"PeriodicalIF":3.5,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10141-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation","authors":"Marco Rosano,&nbsp;Antonino Furnari,&nbsp;Luigi Gulino,&nbsp;Corrado Santoro,&nbsp;Giovanni Maria Farinella","doi":"10.1007/s10514-023-10147-z","DOIUrl":"10.1007/s10514-023-10147-z","url":null,"abstract":"<div><p>Robot visual navigation is a relevant research topic. Current deep navigation models conveniently learn the navigation policies in simulation, given the large amount of experience they need to collect. Unfortunately, the resulting models show a limited generalization ability when deployed in the real world. In this work we explore solutions to facilitate the development of visual navigation policies trained in simulation that can be successfully transferred in the real world. We first propose an efficient evaluation tool to reproduce realistic navigation episodes in simulation. We then investigate a variety of deep fusion architectures to combine a set of mid-level representations, with the aim of finding the best merge strategy that maximize the real world performances. Our experiments, performed both in simulation and on a robotic platform, show the effectiveness of the considered mid-level representations-based models and confirm the reliability of the evaluation tool. The 3D models of the environment and the code of the validation tool are publicly available at the following link: https://iplab.dmi.unict.it/EmbodiedVN/.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1483 - 1502"},"PeriodicalIF":3.5,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10147-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Large language models for chemistry robotics","authors":"Naruki Yoshikawa,&nbsp;Marta Skreta,&nbsp;Kourosh Darvish,&nbsp;Sebastian Arellano-Rubach,&nbsp;Zhi Ji,&nbsp;Lasse Bjørn Kristensen,&nbsp;Andrew Zou Li,&nbsp;Yuchi Zhao,&nbsp;Haoping Xu,&nbsp;Artur Kuramshin,&nbsp;Alán Aspuru-Guzik,&nbsp;Florian Shkurti,&nbsp;Animesh Garg","doi":"10.1007/s10514-023-10136-2","DOIUrl":"10.1007/s10514-023-10136-2","url":null,"abstract":"<div><p>This paper proposes an approach to automate chemistry experiments using robots by translating natural language instructions into robot-executable plans, using large language models together with task and motion planning. Adding natural language interfaces to autonomous chemistry experiment systems lowers the barrier to using complicated robotics systems and increases utility for non-expert users, but translating natural language experiment descriptions from users into low-level robotics languages is nontrivial. Furthermore, while recent advances have used large language models to generate task plans, reliably executing those plans in the real world by an embodied agent remains challenging. To enable autonomous chemistry experiments and alleviate the workload of chemists, robots must interpret natural language commands, perceive the workspace, autonomously plan multi-step actions and motions, consider safety precautions, and interact with various laboratory equipment. Our approach, <span>CLAIRify</span>, combines automatic iterative prompting with program verification to ensure syntactically valid programs in a data-scarce domain-specific language that incorporates environmental constraints. The generated plan is executed through solving a constrained task and motion planning problem using PDDLStream solvers to prevent spillages of liquids as well as collisions in chemistry labs. We demonstrate the effectiveness of our approach in planning chemistry experiments, with plans successfully executed on a real robot using a repertoire of robot skills and lab tools. Specifically, we showcase the utility of our framework in pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization. Further details about <span>CLAIRify</span> can be found at https://ac-rad.github.io/clairify/.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1057 - 1086"},"PeriodicalIF":3.5,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10136-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Semantic anomaly detection with large language models","authors":"Amine Elhafsi,&nbsp;Rohan Sinha,&nbsp;Christopher Agia,&nbsp;Edward Schmerling,&nbsp;Issa A. D. Nesnas,&nbsp;Marco Pavone","doi":"10.1007/s10514-023-10132-6","DOIUrl":"10.1007/s10514-023-10132-6","url":null,"abstract":"<div><p>As robots acquire increasingly sophisticated skills and see increasingly complex and varied environments, the threat of an edge case or anomalous failure is ever present. For example, Tesla cars have seen interesting failure modes ranging from autopilot disengagements due to inactive traffic lights carried by trucks to phantom braking caused by images of stop signs on roadside billboards. These system-level failures are not due to failures of any individual component of the autonomy stack but rather system-level deficiencies in semantic reasoning. Such edge cases, which we call <i>semantic anomalies</i>, are simple for a human to disentangle yet require insightful reasoning. To this end, we study the application of large language models (LLMs), endowed with broad contextual understanding and reasoning capabilities, to recognize such edge cases and introduce a monitoring framework for semantic anomaly detection in vision-based policies. Our experiments apply this framework to a finite state machine policy for autonomous driving and a learned policy for object manipulation. These experiments demonstrate that the LLM-based monitor can effectively identify semantic anomalies in a manner that shows agreement with human reasoning. Finally, we provide an extended discussion on the strengths and weaknesses of this approach and motivate a research outlook on how we can further use foundation models for semantic anomaly detection. Our project webpage can be found at https://sites.google.com/view/llm-anomaly-detection. \u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1035 - 1055"},"PeriodicalIF":3.5,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135322901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Reinforcement learning for shared autonomy drone landings","authors":"Kal Backman,&nbsp;Dana Kulić,&nbsp;Hoam Chung","doi":"10.1007/s10514-023-10143-3","DOIUrl":"10.1007/s10514-023-10143-3","url":null,"abstract":"<div><p>Novice pilots find it difficult to operate and land unmanned aerial vehicles (UAVs), due to the complex UAV dynamics, challenges in depth perception, lack of expertise with the control interface and additional disturbances from the ground effect. Therefore we propose a shared autonomy approach to assist pilots in safely landing a UAV under conditions where depth perception is difficult and safe landing zones are limited. Our approach is comprised of two modules: a perception module that encodes information onto a compressed latent representation using two RGB-D cameras and a policy module that is trained with the reinforcement learning algorithm TD3 to discern the pilot’s intent and to provide control inputs that augment the user’s input to safely land the UAV. The policy module is trained in simulation using a population of simulated users. Simulated users are sampled from a parametric model with four parameters, which model a pilot’s tendency to conform to the assistant, proficiency, aggressiveness and speed. We conduct a user study (<span>(n=28)</span>) where human participants were tasked with landing a physical UAV on one of several platforms under challenging viewing conditions. The assistant, trained with only simulated user data, improved task success rate from 51.4 to 98.2% despite being unaware of the human participants’ goal or the structure of the environment a priori. With the proposed assistant, regardless of prior piloting experience, participants performed with a proficiency greater than the most experienced unassisted participants.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1419 - 1438"},"PeriodicalIF":3.5,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10143-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135510764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Why ORB-SLAM is missing commonly occurring loop closures?","authors":"Saran Khaliq,&nbsp;Muhammad Latif Anjum,&nbsp;Wajahat Hussain,&nbsp;Muhammad Uzair Khattak,&nbsp;Momen Rasool","doi":"10.1007/s10514-023-10149-x","DOIUrl":"10.1007/s10514-023-10149-x","url":null,"abstract":"<div><p>We analyse, for the first time, the popular loop closing module of a well known and widely used open-source visual SLAM (ORB-SLAM) pipeline. Investigating failures in the loop closure module of visual SLAM is challenging since it consists of multiple building blocks. Our meticulous investigations have revealed a few interesting findings. Contrary to reported results, ORB-SLAM frequently misses large fraction of loop closures on public (KITTI, TUM RGB-D) datasets. One common assumption is, in such scenarios, the visual place recognition (vPR) block of the loop closure module is unable to find a suitable match due to extreme conditions (dynamic scene, viewpoint/scale changes). We report that native vPR of ORB-SLAM is not the sole reason for these failures. Although recent deep vPR alternatives achieve impressive matching performance, replacing native vPR with these deep alternatives will only partially improve loop closure performance of visual SLAM. Our findings suggest that the problem lies with the subsequent relative pose estimation module between the matching pair. ORB-SLAM3 has improved the recall of the original loop closing module. However, even in ORB-SLAM3, the loop closing module is the major reason behind loop closing failures. Surprisingly, using <i>off-the-shelf</i> ORB and SIFT based relative pose estimators (non real-time) manages to close most of the loops missed by ORB-SLAM. This significant performance gap between the two available methods suggests that ORB-SLAM’s pipeline can be further matured by focusing on the relative pose estimators, to improve loop closure performance, rather than investing more resources on improving vPR. We also evaluate deep alternatives for relative pose estimation in the context of loop closures. Interestingly, the performance of deep relocalization methods (e.g. MapNet) is worse than classic methods even in loop closures scenarios. This finding further supports the fundamental limitation of deep relocalization methods recently diagnosed. Finally, we expose bias in well-known public dataset (KITTI) due to which these commonly occurring failures have eluded the community. We augment the KITTI dataset with detailed loop closing labels. In order to compensate for the bias in the public datasets, we provide a challenging loop closure dataset which contains challenging yet commonly occurring indoor navigation scenarios with loop closures. We hope our findings and the accompanying dataset will help the community in further improving the popular ORB-SLAM’s pipeline.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1519 - 1535"},"PeriodicalIF":3.5,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Reinforcement learning with model-based feedforward inputs for robotic table tennis","authors":"Hao Ma,&nbsp;Dieter Büchler,&nbsp;Bernhard Schölkopf,&nbsp;Michael Muehlebach","doi":"10.1007/s10514-023-10140-6","DOIUrl":"10.1007/s10514-023-10140-6","url":null,"abstract":"<div><p>We rethink the traditional reinforcement learning approach, which is based on optimizing over feedback policies, and propose a new framework that optimizes over feedforward inputs instead. This not only mitigates the risk of destabilizing the system during training but also reduces the bulk of the learning to a supervised learning task. As a result, efficient and well-understood supervised learning techniques can be applied and are tuned using a validation data set. The labels are generated with a variant of iterative learning control, which also includes prior knowledge about the underlying dynamics. Our framework is applied for intercepting and returning ping-pong balls that are played to a four-degrees-of-freedom robotic arm in real-world experiments. The robot arm is driven by pneumatic artificial muscles, which makes the control and learning tasks challenging. We highlight the potential of our framework by comparing it to a reinforcement learning approach that optimizes over feedback policies. We find that our framework achieves a higher success rate for the returns (<span>(100%)</span> vs. <span>(96%)</span>, on 107 consecutive trials, see https://youtu.be/kR9jowEH7PY) while requiring only about one tenth of the samples during training. We also find that our approach is able to deal with a variant of different incoming trajectories.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1387 - 1403"},"PeriodicalIF":3.5,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10140-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135995053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"RoLoMa: robust loco-manipulation for quadruped robots with arms","authors":"Henrique Ferrolho,&nbsp;Vladimir Ivan,&nbsp;Wolfgang Merkt,&nbsp;Ioannis Havoutis,&nbsp;Sethu Vijayakumar","doi":"10.1007/s10514-023-10146-0","DOIUrl":"10.1007/s10514-023-10146-0","url":null,"abstract":"<div><p>Deployment of robotic systems in the real world requires a certain level of robustness in order to deal with uncertainty factors, such as mismatches in the dynamics model, noise in sensor readings, and communication delays. Some approaches tackle these issues <i>reactively</i> at the control stage. However, regardless of the controller, online motion execution can only be as robust as the system capabilities allow at any given state. This is why it is important to have good motion plans to begin with, where robustness is considered <i>proactively</i>. To this end, we propose a metric (derived from first principles) for representing robustness against external disturbances. We then use this metric within our trajectory optimization framework for solving complex loco-manipulation tasks. Through our experiments, we show that trajectories generated using our approach can resist a greater range of forces originating from any possible direction. By using our method, we can compute trajectories that solve tasks as effectively as before, with the added benefit of being able to counteract stronger disturbances in worst-case scenarios.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1463 - 1481"},"PeriodicalIF":3.5,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10146-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136185248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}