Effective transmission line (TL) inspection in complex corridor environments is essential for ensuring reliable power delivery. This work presents a 3D-based perception method for this task. The proposed method is designed by considering two key characteristics of TL inspection. First, the point cloud data are sparse and class distributions are highly imbalanced, which weakens the signals from thin conductors and tower components. To address this issue, we model long-range spatial relations along the corridor to mitigate data sparsity and imbalance. Second, strong structural correlations exist between conductors and towers, which can be leveraged to improve perception performance. To exploit this property, we construct a unified 3-D representation that jointly models towers, conductors, and vegetation, while fusing Cartesian and polar geometries through geometry-aware alignment. Experiments on real-world corridor datasets demonstrate that the proposed method, termed multigeometric and long-distance TL perception Network (MGLD-TLNet), consistently improves stability and accuracy under conditions of sparsity, occlusion, and complex environmental interactions.

This article addresses the leader–follower consensus problem for a class of nonlinear multiagent systems (MASs) whose collective behavior is modeled by a diffusion partial differential equation (PDE). Existing control strategies for such systems often suffer from high communication overhead and a lack of robustness to unknown nonlinearities and disturbances. To overcome these limitations, we introduce a novel adaptive control scheme that integrates a dynamic event-triggered mechanism with a radial basis function neural network (RBFNN) approximator. The dynamic event trigger scheme significantly reduces communication burdens by aperiodically updating the control signal only at specific moments, while the RBFNN is employed to effectively compensate for the unknown boundary function and unmodeled disturbances. We provide a rigorous Lyapunov-based stability analysis to prove that the proposed controller guarantees stability of the closed-loop system. Numerical simulations demonstrate the efficacy of the proposed method, showing a substantial reduction in communication frequency while ensuring precise consensus tracking.

Aerial-terrestrial amphibious robots excel in search and rescue tasks in unstructured terrains but face challenges in autonomous navigation indoors. Traditional full-mapping methods can degrade global path planning performance, especially when semi-static obstacles shift, leading to suboptimal paths. We propose a method for constructing building structure grid maps that are unaffected by semistatic obstacles. Our approach includes a building structure recognition algorithm based on an octree structure to differentiate between occupied and free grid cells. Experimental results demonstrate that coverage path planning on building structure grid maps produces superior global paths compared to traditional grid maps, offering a more streamlined and robust solution for autonomous navigation of aerial-terrestrial amphibious robots in indoor environments.

Motion planning for continuous contact-based aerial manipulators on complex unstructured surfaces remains a substantial challenge due to the sophisticated topology of unstructured surfaces. While direct planning in the high-dimensional configuration space manifolds faces efficiency limitations, simplified planning in the parametric space sacrifices trajectory quality. Therefore, this paper proposes a sampling-based motion planning method, namely, parameter-configuration space fast marching tree (PCS-FMT*), which integrates both configuration and parameter space information. The proposed PCS-FMT* introduces a reparameterization strategy that compresses the planning space into a low-dimensional parameter manifold while preserving metric consistency with the original configuration space. Thus, PCS-FMT* can efficiently plan in the parameter space and optimize the motion trajectory. Simulations on challenging unstructured surfaces validate the effectiveness of PCS-FMT* for aerial manipulators in contact with unstructured surfaces.

Global Navigation Satellite System (GNSS) positioning is widely used in various applications, but its positioning accuracy is often compromised by Non-Line-of-Sight (NLOS) signals, particularly in urban environments. To address this challenge, we conduct a physical analysis to construct features that reflect NLOS signals. Based on this, we propose a hybrid architecture that integrates Transformer-based self-attention mechanisms with a Mixture-of-Experts (MoE) framework, referred to as TransMoE, for NLOS signal detection. Additionally, to enhance the interpretability of TransMoE, we assign each feature with learnable parameters that dynamically update their weights. Furthermore, we propose an adaptive NLOS weighting algorithm that prioritizes satellites with favorable geometric distributions while mitigating NLOS-contaminated measurements in positioning solutions. Experimental validation on Hong Kong UrbanNav datasets demonstrates that TransMoE achieves consistent detection accuracy exceeding 90% across varying urban canyon scenarios. When integrated into positioning workflows, the adaptive correction algorithm reduces 2D positioning errors by 42.73%, 27.43%, and 40.68% compared to the conventional weighted least squares method.

This paper presents an intelligent control scheme for multirotors, where accurate trajectory tracking, strong robustness and reliable generalization are guaranteed by the dual-feedback sliding-mode (DFSM) guided deep reinforcement learning (RL). Different from current solutions, the proposed method explores optimal learning strategy on the sliding surface according to the DFSM demonstrations, where the elegantly designed parallel evaluation takes full advantage of model knowledge and learning exploration. Specifically, the intelligent tracking control is achieved in a two-step design. First, the DFSM algorithm is designed for multirotors, where the linear and nonlinear feedback terms work cooperatively. Second, the DFSM-guided deep RL is put forward to achieve intelligent switching on the sliding surface, where position and velocity errors are both considered to generate accurate switching decisions. In the framework, explorations and the DFSM demonstrations are evaluated in parallel, where only the explorations that are better than the DFSM baseline, are kept for policy improvement. In this way, the DFSM algorithm keeps pushing the RL policy to explore better strategy, where the unavoidable bad experiences arisen from exploration are identified accurately. Practical comparative experimental results are included to verify the effectiveness of the proposed strategy.Note to Practitioners—This paper is motivated by the practical problem of controlling multirotor system in uncertain environments. Up until now, most existing approaches are proposed without taking full advantage of model knowledge and deep learning techniques simultaneously, which lacks of reliability in practical application. To deal with the problem, a new dual-feedback sliding-mode (DFSM) guided deep reinforcement learning (RL) strategy is proposed, where the dual feedback and guided RL are designed to achieve satisfactory tracking control and simultaneously handle uncertainties. Specifically, by introducing double-check framework, the RL strategy explores optimal switching policy on the sliding surface according to the DFSM demonstrations, guaranteeing strong robustness and reliable generalization of the obtained RL policy in uncertain environments. The key feature of the framework is that the DFSM-driven training guarantees practice-oriented tracking control in a DFSM-RL cooperative manner. Comparative experiments are implemented to verify the tracking performance of the proposed intelligent control strategy.

Aerial transportation technology is the lifeline of air disaster rescue. In this article, a robust adaptive tracking control scheme using backstepping sliding mode techniques is proposed for a quadrotor-based aerial transportation system with a cable-suspended payload in disaster rescue, where the payload is ensured to be driven to predefined trajectories in the presence of strong coupling, uncertainties, and external disturbances. The quadrotor and the payload are modeled as a rigid body and a point mass, respectively, and the two coupling terms between the virtual input of the payload position loop and the payload attitude error as well as between the input force and the quadrotor attitude error are analyzed owing to the underactuated of the quadrotor-based transportation system. Then, adaptive backstepping sliding mode control strategies are designed for the position and swing dynamics of the payload to guarantee payload trajectory tracking, and an observer-based geometric attitude control method is presented for the quadrotor attitude dynamics to ensure the global attitude stability of the system, where prior information about disturbances is not required. The closed-loop stability of the whole system is strictly proven. Finally, real-world experiments are conducted to verify the feasibility and robustness of the proposed control scheme.Note to Practitioners—The motivation of this article is to investigate a robust and adaptive control tracking scheme for aerial transportation systems with a cable-suspended payload in disaster rescue. In most of the existing aerial transportation control schemes with a cable-suspended payload, the payload is driven to follow a desired trajectory while only considering the coupling effect between the aerial platform and the payload. However, in practical disaster rescue applications, the aerial transportation system is inevitably affected by strong coupling, uncertainties, and external disturbances. Meanwhile, to the authors’ best knowledge, there exist few studies that investigate payload following issues while considering strong coupling, uncertainties, and external disturbances simultaneously. Therefore, this article proposes a robust and adaptive tracking control scheme using backstepping sliding mode techniques for a quadrotor-based aerial transportation system with a cable-suspended payload to ensure the stable and accurate payload following control under strong coupling, uncertainties, and external disturbances, where prior information about disturbances is not required under the proposed scheme. The closed-loop stability of the whole system is strictly and mathematically analyzed as well as real-world experiments provide promising results. Moreover, the proposed scheme provides a more realistic setup for autonomous aerial transportation with cable-suspended supplies in disaster rescue.

This article aims to investigate the research achievements related to aerial robots with physical interaction. Various morphologies of aerial physical interaction (APhI) robot prototypes with fixed wing, flapping wing, single main rotor, conventional underactuated multirotor, fully actuated multirotor, even deformed multirotor, and multiple platforms are reviewed for different APhI tasks associated with momentary, loose, and strong interaction coupling. This review also covers APhI robot rigid dynamics and robot-environment coupled interaction dynamics modeling methods, interaction wrench measurement/estimation, decoupled and coupled control, active aerial interaction control, and task-constrained planning approaches. Finally, future development directions and prospects are initially anticipated for aerial robots with physical interaction.Note to Practitioners—Aerial physical interaction (APhI) has been a hot topic in the field of aerial robots in recent years, which is a reflection of the advanced capabilities of aerial robots. However, APhI robots face challenges such as difficulty in flight stability and weak adaptability to dynamic environments while exerting active influence on environments. Under this background, this review aims to offer a reference for researchers and practitioners engaged in the related field from the aspects of system design, modeling, control, and task-constrained planning, which hopes to help them apply APhI robots to polar scientific expeditions, complex environment sampling, infrastructure inspection and maintenance, and other application areas. Further, this review also highlights the design idea of rigid-soft integrated APhI robots from the perspective of design-mechanism-performance to enhance interaction stability and safety.

Homography estimation for multimodal images poses a considerable challenge in computer vision because of content disparities and the diverse feature points captured by different sensors. Existing methods typically extract feature maps using neural networks and apply the Lucas-Kanade (LK) algorithm, which is based on the brightness constancy assumption, to solve the homography matrix. However, applying this assumption across all pixel features in multimodal images can lead to inaccuracies, as these images often contain noise, such as homogeneous regions or considerable appearance variations, which can corrupt the network’s training. To address this problem, we propose an uncertainty-guided deep LK (UG-DLK) framework that integrates uncertainty predictions to enhance the network’s iterative learning process. Specifically, we employ a probabilistic approach where the network predicts the distribution of the feature map rather than fixed values. By designing an uncertainty neighborhood estimator, we unfold the cost volume along the channels into 2-D slices, allowing the model to focus on neighborhood information at specific locations, effectively reducing the interference from spatial neighborhoods in the estimation of feature uncertainty. Through uncertainty modeling, the network can accurately identify scenes and objects that comply with the brightness constancy constraint, leading to more robust learning outcomes. Additionally, we introduce a novel loss function that incorporates feature uncertainty, leading to a smoother optimization landscape near the true homography parameters and reducing convergence oscillations. Our method, which is evaluated on benchmark datasets such as Google Maps, Google Earth, MSCOCO, and DPDN, demonstrates state-of-the-art performance, confirming the robustness and adaptability of our model across various scenarios.

The classification of tree species for overhead transmission lines (OHTL) is of great significance, facilitating the the timely removal of safety hazards posed by trees on power lines. Addressing the challenges in classifying OHTL line tree species, including subtle differences in target shape appearance, densely distributed targets, and limited representation in single-modal data, this article proposes a tree species classification network, VSLNet, based on multimodal data fusion. VSLNet constructs three asymmetric branches, which automatically select more discriminative features among spectra during spectral information processing, and jointly guide the extracted visible light information, ensuring global and local consistency for accurate multispectral classification. Furthermore, in LiDAR processing, the segmentation of individual trees contributes data such as tree height and crown diameter, and seamlessly integrates GPS data with multispectral classification results. Experimental results demonstrate that VSLNet is a feasible and reliable solution for tree classification, with potential applicability to other multimodal tasks.

Accurate semantic segmentation is crucial in distribution network line monitoring to ensure the system’s reliability and security. Due to the complexity of the environment and the diversity of devices, unimodal images (such as RGB images) struggle to provide enough information for effective segmentation. To address these challenges, the complementary nature of RGB and thermal infrared (TIR) images is leveraged to significantly enhance segmentation performance. Therefore, an innovative cross-level multimodal fusion network (CLMFNet) is proposed to improve the accuracy and robustness of semantic segmentation by integrating RGB and TIR data. A dual-branch architecture is employed in CLMFNet to extract features from both RGB and TIR images, which are then effectively integrated through a multimodal fusion strategy. Additionally, a cross-layer guidance mechanism is introduced to facilitate the complementation and optimization of features across different levels. CLMFNet was validated on a custom RGB-T dataset, and experimental results showed that it outperformed state-of-the-art methods in key metrics such as mean accuracy (mAcc) and mean intersection over union (mIoU), demonstrating its effectiveness in performing semantic segmentation in complex power distribution scenarios.

This article proposes a novel hierarchical motion planning strategy for multirotors, where the virtual goal (VG) oriented deep reinforcement learning (RL) and motion optimization are designed cooperatively to achieve efficient, flexible and smooth navigation in unknown environments. Specifically, the intelligent hierarchical motion planning is achieved in a three-step design. First, the dynamic VG generation algorithm is proposed considering the perception range of onboard sensors and current velocity, which transforms the global navigation into a real-time point-to-VG planning, thereby guaranteeing efficient computation even in resource-limited multirotors. Second, instead of generating motion actions, the upper-layer deep RL is designed to make spatial-temporal decisions of VG online, which outputs time allocation and spatial distribution commands according to current observation. Third, based on upper-layer's decisions, local optimization and control are implemented accordingly. Different from existing solutions, high-performance planning is guaranteed by the online VG oriented intelligent decision making, where the data-driven learning and model-driven optimization are integrated to navigate the multirotors. Comparative experiments are carried out in both physical simulation and indoor environments, which demonstrate the satisfactory performance of the proposed motion planning strategy in terms of feasibility, efficiency, navigation smoothness, and flexibility.

The power industry relies on efficient inspection systems to ensure stability and safety. While deep learning has advanced automated inspection, its reliance on custom modules for specific tasks can impact efficiency. Knowledge distillation (KD) offers a balanced solution, but the complex textures and structures of power equipment challenge conventional KD methods, which often fail to capture essential local semantic and topological relationships. To address this, we proposeTopNet, a novel topology-preserved KD framework for power scene detection tasks. Specifically, we model the teacher’s knowledge as a graph, where nodes encode local fine-grained features and edges capture global topological relationships. Based on this, we introduce node feature distillation and edge feature distillation to transfer local–global structural knowledge, which can enhance the student’s ability to perceive objects. Furthermore, we also introduce aggregated feature distillation to incorporate and transfer contextual semantic knowledge. Comprehensive experiments are conducted on two different benchmark datasets to demonstrate that TopNet achieves state-of-the-art detection performance with high efficiency, offering a robust solution for automated power equipment inspection.

As the most fundamental supporting infrastructure of the distribution network, power towers require regular checks of their tilting status to ensure the system’s smooth operation. To overcome distinguishing objects in large-scale scenes using solely images or point clouds poses significant difficulties, we propose a multimodal fusion semantic segmentation network (MFSS) for power tower semantic segmentation and inclination detection. First, effective near-ground filtering and fixed-area slicing algorithms are proposed to address the issues of sample imbalance and insufficient data. Second, MFSS integrates RGB information and point cloud features to enhance the descriptive ability of the tower. Finally, a novel inclination detection method for distribution towers is proposed, estimating tower tilt from the axis between top and bottom centroids to improve accuracy and stability. Experimental results on our constructed dataset show that the proposed method outperforms existing algorithms, achieving 77.3% IoU and 96.69% per-class accuracy in tower segmentation. The mean angle deviation for tilt detection is 0.78$^{\circ }$, with a state judgment false rate of just 2.4%.

Abstract At present, the multi‐object tracking method based on transformer generally uses its powerful self‐attention mechanism and global modelling ability to improve the accuracy of object tracking. However, most existing methods excessively rely on hardware devices, leading to an inconsistency between accuracy and speed in practical applications. Therefore, a lightweight transformer joint position awareness algorithm is proposed to solve the above problems. Firstly, a joint attention module to enhance the ShuffleNet V2 network is proposed. This module comprises the spatio‐temporal pyramid module and the convolutional block attention module. The spatio‐temporal pyramid module fuses multi‐scale features to capture information on different spatial and temporal scales. The convolutional block attention module aggregates channel and spatial dimension information to enhance the representation ability of the model. Then, a position encoding generator module and a dynamic template update strategy are proposed to solve the occlusion. Group convolution is adopted in the input sequence through position encoding generator module, with each convolution group responsible for handling the relative positional relationships of a specific range. In order to improve the reliability of the template, dynamic template update strategy is used to update the template at the appropriate time. The effectiveness of the approach is validated on the MOT16, MOT17, and MOT20 datasets.

In this article, an adaptive force tracking impedance control strategy is investigated for an aerial manipulator in physical interaction with uncertain contact environments. Based on the modified target impedance model, an adaptive impedance control method is proposed to accomplish aerial interaction in uncertain environments while maintaining a stable contact force, wherein the environment parameters of location and stiffness are estimated online to generate a reference position trajectory. Then, in order to ensure the tracking performance of the aerial manipulator, a robust pose tracking controller is designed, including a barrier function-based position controller and an adaptive attitude controller. Both proposed position and attitude controllers can ensure finite-time convergence of the state variable without the priori boundary information of disturbances. In particular, the position state variable can converge to a predefined neighborhood of zero from any initial state, and the control gain is not overestimated. The stability of the proposed strategy is analyzed via Lyapunov tools. Simulations and real-world experiments are conducted to illustrate the feasibility and performance of the proposed control strategy.Note to Practitioners—The motivation of this article is to investigate an adaptive force tracking impedance control strategy for aerial physical interaction with uncertain contact environments. In the existing impedance control schemes for aerial manipulators, the environment parameter of location or stiffness is often required to be utilized in controller design. However, in practical cases, the environmental parameters are not known precisely. Thus, this article presents an adaptive impedance method to automatically generate the reference position trajectory and achieve a stable contact force. Additionally, the tracking performance of the aerial manipulator is inevitably subject to uncertainties and disturbances. To ensure tracking convergence, traditional robust controllers generally involve high control gains than the known upper bounds of the disturbances. The main disadvantage of those controllers is that the control gain is often overestimated when the disturbance decreases. To address this issue, a barrier function-based position controller is proposed for the aerial manipulator, where the priori boundary information of disturbances is not needed and the control gain is adaptively adjusted according to the amplitude of disturbances. The stability and convergence of the proposed strategy are analyzed mathematically, and the experiments using an aerial manipulator provide promising results.

Visual servoing technology has widely been employed in manufacturing because it is a flexible, realizability, and low-cost way to improve the intelligence of the industry robot. Nevertheless, a worrisome and overlooked issue is that the loss of visual features in the camera's field of view may lead to the failures of the visual servoing tasks. This article addresses the visual features escaping problem, by implementing an asymmetric barrier Lyapunov function with a field-of-view constraint controller. The asymmetric barrier Lyapunov function defines a tightly specified range for the feature coordinate errors and ensures the transient response of the tracking error as well as enables arbitrary tracking accuracy. It is worth noting that the asymmetric barrier Lyapunov function directly handles the visual-robot-coupled dynamics while guaranteeing system stabilities. Besides, to accommodate the uncertain dynamics derived from a high-dimensional coupled system, an adaptive controller is proposed utilizing fuzzy neural networks with computational efficiency and few training parameters to enhance the control performance. Finally, the effectiveness of the proposed control strategy has been demonstrated through both theoretical analysis and experimental verification.

This article investigates a robust variable impedance control methodology for aerial manipulators to realize compliant and safe interaction tasks. Considering that the stability characteristics are generally overlooked in existing variable impedance controllers of the aerial manipulator, state-independent stability conditions are applied for time-varying impedance profiles to ensure the exponential stability of the desired variable impedance dynamics (DVID) as well as the boundedness of the state variables in the DVID. A command trajectory variable is introduced for converting the impedance control issue to a particular tracking issue, and then, a robust variable impedance controller based on the wrench estimator is designed to guarantee the exponential convergence of the translational states and impedance error of the aerial manipulator. The designed impedance controller is structurally simple and results in low implementation costs. Next, an improved attitude control approach with the command filter is developed for global flight attitude stability without any singularities or ambiguities, where the filter is introduced to avoid computing the derivative signals of the generalized force input. Finally, the effectiveness of the proposed control method is illustrated via numerical simulations and interaction experiments with different targets in real scenarios.

In this article, an image-based visual servoing (IBVS) control strategy is proposed for the unmanned aerial manipulator (UAM) system to track and grasp a moving target. Specifically, a robust-adaptive velocity observer is designed to estimate the relative velocity between the tracked target and the UAM platform. Based on the velocity observer, an IBVS controller using onboard camera of the UAM platform is proposed for moving target tracking without velocity measurement. Then, the barrier Lyapunov function is introduced into the UAM platform IBVS controller to ensure the safety of target tracking. Besides, another virtual camera is constructed on manipulator end-effector to compensate for the tracking error of the UAM platform. As a benefit, the eye-to-hand onboard camera ensures the global view of the UAM, and the eye-in-hand virtual camera of the manipulator ensures the accuracy of the grasping task. Finally, the stability of the proposed IBVS control strategy is analyzed through Lyapunov theory. The comparative simulations are provided to illustrate the target tracking performance of the proposed method. The experimental results demonstrate that the proposed method can be applied to the UAM with a low-cost sensor suite to realize the tasks of tracking and grasping a moving target.

This article addresses the vision-based landing problem of a low-cost quadrotor on an unknown moving platform. A robust landing controller is developed, which consists of the design of the low-complexity outer-loop controller and the geometric inner-loop attitude controller. First, an error transformation based on prescribed performance is designed to guarantee the landing behaviors and deal with the intermediate control signal of backstepping approaches, resulting in a low-complexity position-based visual servoing (PBVS) design. In addition, the proposed PBVS controller exhibits strong robustness against an uncertain relative dynamic system due to no incorporation of any prior knowledge of the moving platform. Next, a modified geometric attitude controller is presented by characterizing the geometric properties of rotation matrices intrinsically. Finally, the stability analysis is presented using Lyapunov stability theory, and the effectiveness of the proposed control strategy is demonstrated through numerical simulations and experiments.

This article aims to solve the problem of formation control of mobile robots based on image and provide a low-cost as well as ease-of-implementation solution for mobile robots relying merely on a monocular camera under field-of-view (FOV) constraints. A low-complexity image-based visual servo controller is proposed, which can achieve the desired relative position on the image plane and solve the FOV constraints without the feature depth and leader’s velocities information. To facilitate the control design, a state transformation is first performed to decouple the visual motion kinematics. Then, an error transformation is introduced to handle the FOV constraints, and performance specifications are incorporated in the error transformation to achieve the predefined control performance. Finally, a simple static controller is derived using only information from images, and the stability of the uncertain system with unknown control direction/coefficients under the given performance control condition is analyzed. The effectiveness and performance of the proposed visual servoing controller can be illustrated using both simulations and experiments.

Defects on rail surfaces, which have become critical problems, need to be detected and removed as quickly as possible to ensure the fast, safe, and stable operation of trains. At present, although many solutions have been proposed to address these problems, the comprehensiveness, rapidity, and accuracy of defect detection remain unsatisfactory. This study aims to resolve these existing problems and accordingly proposes a multi-model rail surface defect detection system based on convolutional neural networks (MRSDI-CNN) from the standpoint of studying the squat on the rail surface. The convolutional neural networks utilized include the improved Single Shot MultiBox Detector (SSD) and You Only Look Once version 3(YOLOv3)—two types of one-stage networks. We expounded and analyzed the performance of the convolutional neural networks as well as their applicability to rail surface defect detection. We used a diverse range of rail defect sizes to improve the detection performance of the two deep learning networks, following which they could identify three types of squats in parallel with improved accuracy and without reduction of the detection speed. The experimental results confirm the effectiveness and superiority of the proposed method over those of previous studies.

The problem of consensus in networked agent systems is revisited and applied to vision-based localization. A class of new consensus dynamics is introduced first, and sufficient conditions including the persistence of excitation on the coupling matrix for reaching consensus are derived. As an application of the proposed consensus dynamics, an adaptive localization algorithm then is proposed for autonomous robots equipped with primarily visual sensors in GPS-denied environments. In the context of consensus over an undirected tree topology, the convergence of the proposed localization algorithm is proved. Finally, both numerical simulations and physical experiments are presented to show the effectiveness of the proposed localization algorithm. Our algorithm is simpler to implement and computationally cheaper compared to other localization methods. Moreover, it is immune to error accumulation and long-term stable, and the asymptotical convergence of the estimation errors can be theoretically guaranteed.

Defects on the surface of fabrics seriously affect the production speed and quality of textile products. There are many difficulties in the detection of surface defects on fabrics, such as substantial differences in length-width ratio, uneven distribution, and few features. However, existing methods have the disadvantages of slow detection speed and high misdetection rate. This present study proposes a method of integrating deformable convolution and pyramid network in Cascade R-CNN (IDPNet) for fabric defect detection. First, image data are labeled according to the type and distribution of defects. Then we design a novel multi-stage object detection architecture named IDPNet to detect defects on the surface of fabrics. In the first stage, Resnet50, in combination with feature pyramid network and deformable convolution is used to improve the detection performance of small defects. Besides, we trained a sequence of detectors with increasing IoUs stage by stage based on Cascade R-CNN in the second stage. Finally, experimental results demonstrate that the proposed neural network equip an outstanding performance against other approaches and achieve the accuracy of 91.57% in fabric defect detection, which proves its utility in practice.

With the development of artificial intelligence, robot swarm systems also frequently appear in complex tasks of different situation. One of the important research directions is the formation of multi-robots. This paper analyzes the limitations of existing algorithms for large-scale mobile robot swarm formation control problems and proposes a consensus control algorithm with two-layer nearest neighbor information. It carries out experimental simulation to verify its convergence performance. At the same time, combined with a distributed structure control strategy that can change the number of robot formation members, the formation control experiment is carried out on the experimental platform consisted of robot state information detection device and multiple mobile robots,to further verify its feasibility.