npj Robotics最新文献

{"title":"Efficient data-driven joint-level calibration of cable-driven surgical robots","authors":"Haonan Peng, Andrew Lewis, Yun-Hsuan Su, Shan Lin, Dun-Tin Chiang, Wenfan Jiang, Helen Lai, Blake Hannaford","doi":"10.1038/s44182-024-00016-x","DOIUrl":"10.1038/s44182-024-00016-x","url":null,"abstract":"Accurate joint position estimation is crucial for the control of cable-driven laparoscopic surgical robots like the RAVEN-II. However, any slack and stretch in the cable can lead to errors in kinematic estimation, complicating precise control. This work proposes an efficient data-driven calibration method, requiring no additional sensors post-training. The calibration takes 8–21 min and maintains high accuracy during a 6-hour heavily loaded operating. The Deep Neural Network (DNN) model reduces errors by 76%, achieving accuracy of 0.104∘, 0.120∘, and 0.118 mm for joints 1, 2, and 3, respectively. Compared to end-to-end models, the DNN achieves better accuracy and faster convergence by correcting original inaccurate joint positions. Additionally, a linear regression model offers 160 times faster inference speed than the DNN, suitable for RAVEN’s 1000 Hz control loop, with slight compromises in accuracy. This approach should significantly enhance the accuracy of similar cable-driven robots.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00016-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive morphing of wing and tail for stable, resilient, and energy-efficient flight of avian-inspired drones","authors":"Simon Luis Jeger, Valentin Wüest, Charbel Toumieh, Dario Floreano","doi":"10.1038/s44182-024-00015-y","DOIUrl":"10.1038/s44182-024-00015-y","url":null,"abstract":"Avian-inspired drones feature morphing wing and tail surfaces, enhancing agility and adaptability in flight. Despite their large potential, realising their full capabilities remains challenging due to the lack of generalized control strategies accommodating their large degrees of freedom and cross-coupling effects between their control surfaces. Here we propose a new body-rate controller for avian-inspired drones that uses all available actuators to control the motion of the drone. The method exhibits robustness against physical perturbations, turbulent airflow, and even loss of certain actuators mid-flight. Furthermore, wing and tail morphing is leveraged to enhance energy efficiency at 8 m/s, 10 m/s, and 12 m/s using in-flight Bayesian optimization. The resulting morphing configurations yield significant gains across all three speeds of up to 11.5% compared to non-morphing configurations and display a strong resemblance to avian flight at different speeds. This research lays the groundwork for the development of autonomous avian-inspired drones that operate under diverse wind conditions, emphasizing the role of morphing in improving energy efficiency.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00015-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agile robotic fish based on direct drive of continuum body","authors":"Keisuke Iguchi, Taiki Shimooka, Shuto Uchikai, Yuto Konno, Hiroto Tanaka, Yusuke Ikemoto, Jun Shintake","doi":"10.1038/s44182-024-00014-z","DOIUrl":"10.1038/s44182-024-00014-z","url":null,"abstract":"Fish-like agile movements, such as fast swimming and rapid turning, are essential for biomimetic underwater robots but challenging to achieve simultaneously. We present a self-contained robotic fish capable of swimming at a speed of 6.3 body length per second and pivot turning at an angular speed of 1450° per second. These fast motions, comparable to real fish, are realized by directly oscillating a flexible body using an electromagnetic motor, eliminating the need for transmission parts and simplifying the structure. This direct-drive (DD) method improves mechanical robustness and generates fish-like deformations and subsequent rapid swimming. The Strouhal and swimming numbers of the robot match typical values observed in nature. Moreover, the observed frequency peaks in swimming are similar to computed values using a model, which guides the design of the robot. These results illustrate the DD method as a promising framework for the creation of highly versatile biomimetic underwater robots.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00014-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FALCON: Fourier Adaptive Learning and Control for Disturbance Rejection Under Extreme Turbulence","authors":"Sahin Lale, Peter I. Renn, Kamyar Azizzadenesheli, Babak Hassibi, Morteza Gharib, Anima Anandkumar","doi":"10.1038/s44182-024-00013-0","DOIUrl":"10.1038/s44182-024-00013-0","url":null,"abstract":"Controlling aerodynamic forces in turbulent conditions is crucial for UAV operation. Traditional reactive methods often struggle due to unpredictable flow and sensor noise. We present FALCON (Fourier Adaptive Learning and Control), a model-based reinforcement learning framework for effective modeling and control of aerodynamic forces under turbulent flows. FALCON leverages two key insights: turbulent dynamics are well-modeled in the frequency domain, and most turbulent energy is concentrated in low-frequencies. FALCON learns a concise Fourier basis to model system dynamics from 35 s of flow data. To address sensor limitations, FALCON models dynamics using a short history of actions and measurements. With this approach, FALCON applies model predictive control for safe and efficient control. Tested in the Caltech wind tunnel under highly turbulent conditions, FALCON learns to control the underlying nonlinear dynamics with less than 9 min of data, consistently outperforming state-of-the-art methods. We provide guarantees for FALCON, ensuring stability and robustness.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00013-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-speed aerial grasping using a soft drone with onboard perception","authors":"Samuel Ubellacker, Aaron Ray, James M. Bern, Jared Strader, Luca Carlone","doi":"10.1038/s44182-024-00012-1","DOIUrl":"10.1038/s44182-024-00012-1","url":null,"abstract":"Contrary to the stunning feats observed in birds of prey, aerial manipulation and grasping with flying robots still lack versatility and agility. Conventional approaches using rigid manipulators require precise positioning and are subject to large reaction forces at grasp, which limit performance at high speeds. The few reported examples of high-speed aerial grasping rely on motion capture systems, or fail to generalize across environments and grasp targets. We describe the first example of a soft aerial manipulator equipped with a fully onboard perception pipeline, capable of robustly localizing and grasping visually and morphologically varied objects. The proposed system features a novel passively closed tendon-actuated soft gripper that enables fast closure at grasp, while compensating for position errors, complying to the target-object morphology, and dampening reaction forces. The system includes an onboard perception pipeline that combines a neural-network-based semantic keypoint detector, a state-of-the-art robust 3D object pose estimator, and a fixed-lag smoother to estimate the pose of known objects. The resulting pose estimate is passed to a minimum-snap trajectory planner, tracked by an adaptive controller that fully compensates for the added mass of the grasped object. Finally, a finite-element-based controller determines optimal gripper configurations for grasping. Experiments on three different targets confirm that our approach enables dynamic, high-speed, and versatile grasping, all of which are necessary capabilities for tasks such as rapid package delivery or emergency relief. We demonstrate fully onboard vision-based grasps of a variety of objects, in both indoor and outdoor environments, and up to speeds of 2.0 m/s—the fastest vision-based grasp reported in the literature. Finally, we take a major step in expanding the utility of our platform beyond stationary targets, by demonstrating motion-capture-based grasps of targets moving up to 0.3 m/s, with relative speeds up to 1.5 m/s.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00012-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordinating limbs and spine: (Pareto-)optimal locomotion in theory, in vivo, and in robots","authors":"Robert Rockenfeller, Robert L. Cieri, Johanna T. Schultz, Robin Maag, Christofer J. Clemente","doi":"10.1038/s44182-024-00011-2","DOIUrl":"10.1038/s44182-024-00011-2","url":null,"abstract":"Among vertebrates, patterns of movement vary considerably, from the lateral spine-based movements of fish and salamanders to the predominantly limb-based movements of mammals. Yet, we know little about why these changes may have occurred in the course of evolution. Lizards form an interesting intermediate group where locomotion appears to be driven by both motion of their limbs and lateral spinal undulation. To understand the evolution and relative advantages of limb versus spine locomotion, we developed an empirically informed mathematical model as well as a robotic model and compared in silico predictions to in-vivo data from running and climbing lizards. Our mathematical model showed that, if limbs were allowed to grow to long lengths, movements of the spine did not enable longer strides, since spinal movements reduced the achievable range of motion of the limbs before collision. Yet, in-vivo data show lateral spine movement is widespread among a diverse group of lizards moving on level ground or climbing up and down surfaces. Our climbing robotic model was able to explain this disparity, showing that increased movement of the spine was energetically favourable, being associated with a reduced cost of transport. Our robot model also revealed that stability, as another performance criterion, decreased with increased spine and limb range of motion—detailing the trade-off between speed and stability. Overall, our robotic model found a Pareto-optimal set of strides—when considering speed, efficiency, and stability—requiring both spine and limb movement, which closely agreed with movement patterns among lizards. Thus we demonstrate how robotic models, in combination with theoretical considerations, can reveal fundamental insights into the evolution of movement strategies among a broad range of taxa.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00011-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart insect-computer hybrid robots empowered with enhanced obstacle avoidance capabilities using onboard monocular camera","authors":"Rui Li, Qifeng Lin, Phuoc Thanh Tran-Ngoc, Duc Long Le, Hirotaka Sato","doi":"10.1038/s44182-024-00010-3","DOIUrl":"10.1038/s44182-024-00010-3","url":null,"abstract":"Insect-computer hybrid robots are receiving increasing attention as a potential alternative to small artificial robots due to their superior locomotion capabilities and low manufacturing costs. Controlling insect-computer hybrid robots to travel through terrain littered with complex obstacles of various shapes and sizes is still challenging. While insects can inherently deal with certain obstacles by using their antennae to detect and avoid obstacles, this ability is limited and can be interfered with by control signals when performing navigation tasks, ultimately leading to the robot being trapped in a specific place and having difficulty escaping. Hybrid robots need to add additional sensors to provide accurate perception and early warning of the external environment to avoid obstacles before getting trapped, ensuring smooth navigation tasks in rough terrain. However, due to insects’ tiny size and limited load capacity, hybrid robots are very limited in the sensors they can carry. A monocular camera is suitable for insect-computer hybrid robots because of its small size, low power consumption, and robust information acquisition capabilities. This paper proposes a navigation algorithm with an integrated obstacle avoidance module using a monocular camera for the insect-computer hybrid robot. The monocular cameras equipped with a monocular depth estimation algorithm based on deep learning can produce depth maps of environmental obstacles. The navigation algorithm generates control commands that can drive the hybrid robot away from obstacles according to the distribution of obstacle distances in the depth map. To ensure the performance of the monocular depth estimation model when applied to insect-computer hybrid robotics scenarios, we collected the first dataset from the viewpoint of a small robot for model training. In addition, we propose a simple but effective depth map processing method to obtain obstacle avoidance commands based on the weighted sum method. The success rate of the navigation experiment is significantly improved from 6.7% to 73.3%. Experimental results show that our navigation algorithm can detect obstacles in advance and guide the hybrid robots to avoid them before they get trapped.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00010-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyborg insect repeatable self-righting locomotion assistance using bio-inspired 3D printed artificial limb","authors":"Marc Josep Montagut Marques, Qiu Yuxuan, Hirotaka Sato, Shinjiro Umezu","doi":"10.1038/s44182-024-00009-w","DOIUrl":"10.1038/s44182-024-00009-w","url":null,"abstract":"Cyborg insects have emerged as a promising solution for rescue missions, owing to their distinctive and advantageous mobility characteristics. These insects are outfitted with electronic backpacks affixed to their anatomical structures, which endow them with imperative communication, sensing, and control capabilities essential for effecting survivor retrieval. Nevertheless, the attachment of supplementary loads to the insect’s body can exert adverse effects on their intrinsic self-righting locomotion when confronted with fall or shock scenarios. To address this challenge, the present study introduces a bio-inspired 3D-printed artificial limb that serves to facilitate the maneuverability of cyborg insects amidst unpredictable conditions. Drawing inspiration from the natural self-righting motion exhibited by Coccinellidae, we have successfully identified a solution that can be transferred to the electronic backpack utilized by G. portentosa. Incorporation of the bio-inspired artificial wing-like limb has notably enabled the cyborg insect to achieve a remarkable tilting angle of 112°, thereby significantly amplifying the success ratio of self-righting under conditions closely emulating those prevalent in disaster areas. Moreover, we have replicated the expansion and contraction kinematics to ensure seamless motion progression within confined spaces. Importantly, the fabricated device proffered in this study has been meticulously designed for facile reproducibility employing commonly available tools, thereby serving as an inspirational catalyst for fellow researchers engaged in the advancement of 3D-printed limb development aimed at expanding the functional capacities of cyborg insects.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00009-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141156529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magneto-oscillatory localization for small-scale robots","authors":"F. Fischer, C. Gletter, M. Jeong, T. Qiu","doi":"10.1038/s44182-024-00008-x","DOIUrl":"10.1038/s44182-024-00008-x","url":null,"abstract":"Magnetism is widely used for the wireless localization and actuation of robots and devices for medical procedures. However, current static magnetic localization methods suffer from large required magnets and are limited to only five degrees of freedom due to a fundamental constraint of the rotational symmetry around the magnetic axis. We present the small-scale magneto-oscillatory localization (SMOL) method, which is capable of wirelessly localizing a millimeter-scale tracker with full six degrees of freedom in deep biological tissues. The SMOL device uses the temporal oscillation of a mechanically resonant cantilever with a magnetic dipole to break the rotational symmetry, and exploits the frequency-response to achieve a high signal-to-noise ratio with sub-millimeter accuracy over a large distance of up to 12 centimeters and quasi-continuous refresh rates up to 200 Hz. Integration into real-time closed-loop controlled robots and minimally-invasive surgical tools are demonstrated to reveal the vast potential of the SMOL method.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-024-00008-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration-based model learning with self-attention for risk-sensitive robot control","authors":"DongWook Kim, Sudong Lee, Tae Hwa Hong, Yong-Lae Park","doi":"10.1038/s44182-023-00006-5","DOIUrl":"10.1038/s44182-023-00006-5","url":null,"abstract":"Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics that involve inherent randomness, which may cause instability or malfunction of the target system and render the system susceptible to external perturbations. In this paper, we propose an online model update algorithm that can be directly operated in real-world robot systems. The algorithm leverages a self-attention mechanism embedded in neural networks for the kinematics and the dynamics models of the target system. The approximated model involves redundant self-attention paths to the time-independent kinematics and dynamics models, allowing us to detect abnormalities by calculating the trace values of the self-attention matrices. This approach reduces the randomness during the exploration process and enables the detection and rejection of detected perturbations while updating the model. We validate the proposed method in simulation and with real-world robot systems in three application scenarios: path tracking of a soft robotic manipulator, kinesthetic teaching and behavior cloning of an industrial robotic arm, and gait generation of a legged robot. All of these demonstrations are achieved without the aid of simulation or prior knowledge of the models, which supports the proposed method’s universality for various robotics applications.","PeriodicalId":499869,"journal":{"name":"npj Robotics","volume":" ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44182-023-00006-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138592532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}