Knot-inspired optical sensors for slip detection and friction measurement in dexterous robotic manipulation

Friction plays a critical role in dexterous robotic manipulation.However,realizing friction sensing remains a challenge due to the difficulty in designing sensing structures to decouple multi-axial forces.Inspired by the topological mechanics of knots,we construct optical fiber knot(OFN)sensors for slip detection and friction measurement.By introducing localized self-contacts along the fiber,the knot structure enables anisotropic responses to normal and frictional forces.By employing OFNs and a change point detection algorithm,we demonstrate adaptive robotic grasping of slipping cups.We further develop a robotic finger that can measure tri-axial forces via a centrosymmetric architecture composed of five OFNs.Such a tactile finger allows a robotic hand to manipulate human tools dexterously.This work could provide a straightforward and cost-effective strategy for promoting adaptive grasping,dexterous manipulation,and human-robot interaction with tactile sensing.

Spherical robot: A novel robot for exploration in harsh unknown environments

The authors propose a complete software and hardware framework for a novel spherical robot to cope with exploration in harsh and unknown environments.The proposed robot is driven by a heavy pendulum covered by a fully enclosed spherical shell,which is strongly protected,amphibious,anti-overturn and has a long-battery-life.Algorithms for location and perception,planning and motion control are comprehensively designed.On the one hand,the authors fully consider the kinematic model of a spherical robot,propose a positioning algorithm that fuses data from inertial measurement units,motor encoder and Global Navigation Satellite System,improve global path planning algorithm based on Hybrid A*and design an instruction planning controller based on model predictive control(MPC).On the other hand,the dynamic model is built,linear MPC and robust servo linear quadratic regulator algorithm is improved,and a speed controller and a direction controller are designed.In addition,based on the pose and motion charac-teristics of a spherical robot,a visual obstacle perception algorithm and an electronic image stabilisation algorithm are designed.Finally,the authors build physical systems to verify the effectiveness of the above algorithms through experiments.

A novel distributed architecture for unmanned aircraft systems based on Robot Operating System 2

A novel distributed control architecture for unmanned aircraft system (UASs) based on thenew Robot Operating System (ROS) 2 middleware is proposed, endowed with industrialgradetools that establish a novel standard for high-reliability distributed systems. Thearchitecture has been developed for an autonomous quadcopter to design an inclusivesolution ranging from low-level sensor management and soft real-time operating systemsetup and tuning to perception, exploration, and navigation modules orchestrated by afinite-state machine. The architecture proposed in this study builds on ROS 2 with itsscalability and soft real-time communication functionalities, while including security andsafety features, optimised implementations of localisation algorithms, and integrating aninnovative and flexible path planner for UASs. Finally, experimental results have beencollected during tests carried out both in the laboratory and in a realistic environment,showing the effectiveness of the proposed architecture in terms of reliability, scalability, andflexibility.

A novel robotic visual perception framework for underwater operation

Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual perception.In addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across time.In response to these two problems,we present a design for a novel robotic visual perception framework.First,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection performance.As a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain shift.Moreover,non-reference assessments are proposed for detection continuity and stability based on object tracklets.Further,online tracklet refinement is developed to improve the temporal performance of detectors.Finally,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is established.Small-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and tracking.Extensive experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed approaches.The codes are available at https://github.com/yrqs/VisPerception.

Self-Aligning Multi-Modal Transformer for Oropharyngeal Swab Point Localization

The oropharyngeal swabbing is a pre-diagnostic procedure used to test various respiratory diseases, including COVID and Influenza A (H1N1). To improve the testing efficiency of testing, a real-time, accurate, and robust sampling point localization algorithm is needed for robots. However, current solutions rely heavily on visual input, which is not reliable enough for large-scale deployment. The transformer has significantly improved the performance of image-related tasks and challenged the dominance of traditional convolutional neural networks (CNNs) in the image field. Inspired by its success, we propose a novel self-aligning multi-modal transformer (SAMMT) to dynamically attend to different parts of unaligned feature maps, preventing information loss caused by perspective disparity and simplifying overall implementation. Unlike preexisting multi-modal transformers, our attention mechanism works in image space instead of embedding space, rendering the need for the sensor registration process obsolete. To facilitate the multi-modal task, we collected and annotate an oropharynx localization/segmentation dataset by trained medical personnel. This dataset is open-sourced and can be used for future multi-modal research. Our experiments show that our model improves the performance of the localization task by 4.2% compared to the pure visual model, and reduces the pixel-wise error rate of the segmentation task by 16.7% compared to the CNN baseline.