Ultra-low altitude UAVs (below 120 meters) are gaining importance in the booming low-altitude economy, where GNSS signals are often unreliable or unavailable. Vision-based localization emerges as a promising alternative; however, existing benchmarks are not designed for ultra-low flight and typically adopt pinhole cameras with limited field of view, making them less effective in handling occlusions and repetitive textures near the ground. To address these limitations, we introduce the first panoramic UAV localization dataset tailored for ultra-low altitude scenarios. Built on a four-fisheye-camera system in the high-fidelity RflySim platform, our dataset captures diverse conditions — including day/night cycles, extreme weather, and dynamic obstacles — and contains over hundreds of thousands of frames. It is further enhanced with real-world UAV panoramic data to narrow the sim-to-real gap and will be continuously updated for broader applicability. Comprehensive experiments confirm the effectiveness and transferability of our dataset, establishing it as a robust benchmark for future research in vision-based UAV localization.

As unmanned aerial vehicles (UAVs) lead the way in the development of future digital smart cities, they continue to face scrutiny due to safety concerns. While robotics simulators offer UAVs efficient and cost-effective testing environments, they often lack comprehensive safety design considerations and user testing requirements. In this study, we introduce RflySimSaT, a dedicated safety testing platform for UAVs that addresses safety factors throughout the entire lifecycle. This platform incorporates diverse fault testing scenarios, high-fidelity dynamic models, an integrated safety assessment framework, standardized testing procedures, and customizable interfaces. The modular architecture and deployment of RflySimSaT facilitate plug-and-play cross-platform closed-loop safety testing. Users simply need to supply their aircrafts and autopilots, enabling them to efficiently navigate the phases of development, deployment, testing, and assessment using the customizable modules and standardized processes offered by the platform. To validate RflySimSaT’s credibility and versatility, we designed various test cases that demonstrate its practicality and scalability. Additionally, we provide a comprehensive user manual, detailed case studies, and a rich fault dataset. The platform is open-source and available at: https://github.com/RflySim/RFlySimSafe/tree/RflySimSaT.

Autonomous navigation requires real-time trajectory optimization with limited onboard computational resources, where traditional optimization-based methods often impose heavy computational burdens and require extensive parameter tuning. To achieve transparent navigation results and minimize computational burden, this paper introduces an iterative learning planning (ILP) approach for navigation. By dynamically integrating the control layer into the planning layer, ILP can achieve efficient trajectory optimization through functional iterative learning, providing transparent results that enhance navigation efficiency. This approach significantly improves real-time performance, achieving a time complexity ofO(k*n), wherek*represents the number of iterations andnrepresents the number of waypoints. Simulations and quadcopter experiments are conducted to verify the proposed framework. Results show that ILP significantly improves computational efficiency, ensures safe navigation, and maintains robustness against disturbances, demonstrating its potential as a practical solution for real-world autonomous systems.

In recent times, Lyapunov theory has been in-corporated into learning-based control methods to provide a stability guarantee. However, merely satisfying the Lyapunov conditions does not fully leverage the capabilities of the Neural Network (NN) controller. Furthermore, training an effective Lyapunov candidate requires substantial data, which inherently results in sample inefficiency. To address these limitations, we propose an off-policy variant of the vanilla D-learning method that uses current and historical data to iteratively enhance the NN controller within the framework of Lyapunov theory. Our method outperforms the Deep Deterministic Policy Gradient (DDPG) and D-learning in terms of stability, sample efficiency, and the quality of the trained controllers and Lyapunov candidates. Link to code: github.com/Shenzhaolong1330/DOPT

In this paper, we present DL-Clip, an innovative online learning approach for nonlinear stabilizing control that operates without prior knowledge of system dynamics or reward signals, while significantly improving training efficiency. DL-Clip introduces a novel integration of stabilizing control with efficient Reinforcement Learning (RL) training mechanisms. The algorithm uses Lyapunov functions to ensure system stability and employs clipping operations to optimize policy updates, achieving faster convergence. We evaluate the effectiveness of DL-Clip through experiments, including simulations of the inverted pendulum and the Image-Based Visual Servoing (IBVS) for multicopter position stabilization. In addition, we validate the approach through a real flight experiment based on the IBVS problem, demonstrating its practical applicability.

6D pose estimation is a central problem in robot vision. Compared with pose estimation based on point correspondences or its robust versions, correspondence-free methods are often more flexible. However, existing correspondence-free methods often rely on feature representation alignment or end-to-end regression. For such a purpose, a new correspondence-free pose estimation method and its practical algorithms are proposed, whose key idea is the elimination of unknowns by process of addition to separate the pose estimation from correspondence. By taking the considered point sets as patterns, feature functions used to describe these patterns are introduced to establish a sufficient number of equations for optimization. The proposed method is applicable to nonlinear transformations such as perspective projection and can cover various pose estimations from 3D-to-3D points, 3D-to-2D points, and 2D-to-2D points. Experimental results on both simulation and actual data are presented to demonstrate the effectiveness of the proposed method.

In clinical practice, medical image analysis often requires efficient execution on resource-constrained mobile devices. However, existing mobile models-primarily optimized for natural images-tend to perform poorly on medical tasks due to the significant information density gap between natural and medical domains. Combining computational efficiency with medical imaging-specific architectural advantages remains a challenge when developing lightweight, universal, and high-performing networks. To address this, we propose a mobile model called Mobile U-shaped Vision Transformer (Mobile U-ViT) tailored for medical image segmentation. Specifically, we employ the newly proposed ConvUtr as a hierarchical patch embedding, featuring a parameter-efficient large-kernel CNN with inverted bottleneck fusion. This design exhibits transformer-like representation learning capacity while being lighter and faster. To enable efficient local-global information exchange, we introduce a novel Large-kernel Local-Global-Local (LKLGL) block that effectively balances the low information density and high-level semantic discrepancy of medical images. Finally, we incorporate a shallow and lightweight transformer bottleneck for long-range modeling and employ a cascaded decoder with downsampled skip connections for dense prediction. Despite its reduced computational demands, our medical-optimized architecture achieves state-of-the-art performance across eight public 2D and 3D datasets covering diverse imaging modalities, including zero-shot testing on four unseen datasets. These results establish it as an efficient yet powerful and generalization solution for mobile medical image analysis. Code is available at: https://github.com/FengheTan9/Mobile-U-ViT.

With the rapid development of uncrewed aerial vehicle (UAV) technology in recent years, research on large-scale low-altitude UAV air traffic management (ATM) has gained attention. Unlike the traditional ATM, the number of small UAVs in the airspace may be in the millions, making air traffic management challenging. In an ATM, airspace is composed of airways, intersections, and nodes. In this paper, a three-dimensional (3-D) roundabout model is utilized as an airspace structure for air traffic intersections of known traffic network models, which is decomposed into a central island, several ramps, and buffer zones. In this paper, for simplicity, the distributed coordination of the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass through a 3-D roundabout is focused on, which is formulated as a 3-D roundabout passing-through problem. The corresponding control objectives include inter-agent conflict-free, keeping within the 3-D curved virtual tube, and avoiding local minima. Lyapunov-like functions are designed elaborately, and formal analysisismade to show that all UAVs can pass through the 3-D roundabout without getting trapped. Taking the kinematic model of VTOL UAVs into consideration, the horizontal control and attitude control channels are decoupled, which is more reasonable for practical applications. Numerical simulation and real experiment are given to show the effectiveness of the proposed method.

Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel-wise contrastive learning mainly helps with pixel-wise dense prediction tasks. The counterpart to an instance in instance-level CL is a pixel, along with its neighboring context, in pixel-wise CL. Aiming to build better feature representation, there is a vast literature about designing instance augmentation strategies for instance-level CL; but there is little similar work on pixel augmentation for pixel-wise CL with a pixel granularity. In this paper, we attempt to bridge this gap. We first classify a pixel into three categories, namely low-, medium-, and high-informative, based on the information quantity the pixel contains. We then adaptively design separate augmentation strategies for each category in terms of augmentation intensity and sampling ratio. Extensive experiments validate that our information-guided pixel augmentation strategy succeeds in encoding more discriminative representations and surpassing other competitive approaches in unsupervised local feature matching. Furthermore, our pretrained model improves the performance of both one-shot and fully supervised models. To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning. Code is available at https://github.com/Curli-quan/IGU-Aug.

This paper presents a novel model-free spatial iterative learning (IL) framework to enhance the efficiency of vector field (VF) navigation for mobile robots. By integrating the idea of iterative learning control (ILC) with VF, this framework utilizes historical data to enhance navigation efficiency significantly, reducing traversal time and expanding the applicability of IL to rapid navigation. Importantly, it has low time complexity with$O(n)$per iteration, where$n$denotes the waypoints number, preventing the significant computational overhead caused by the increasing waypoints in existing methods, which often exceeds$O(n^{2})$, making it well-suited for real-time planning. Moreover, the approach is inherently model-free, leaning on historical data, thus enabling agile navigation with limited reliance on intricate model details. The paper presents a comprehensive theoretical analysis of the stability, time optimality, time complexity, parameter insensitivity, robustness, and usage. Extensive simulations and experiments highlight its efficiency, promising a transformative impact on mobile robot navigation through the proposed IL.

Automatic landing of tethered unmanned aerial vehicles (UAVs) is an important issue. Typically, UAVs rely on location sensors such as global navigation satellite system (GNSS) and external cameras to obtain location data. However, harsh environments such as denial GNSS or strong winds make it difficult for UAVs to approach the landing area, and common solutions cannot be used for automatic landing. A tethered lifting-wing multicopter has a structure and static stability similar to a kite. Inspired by kites, this paper proposes a new landing method for tethered lifting-wing multicopters, which can be used without location or velocity sensors. During the landing phase, the tethered lifting-wing multicopter only needs to keep the rotor thrust to actively straighten the tethered cable and a constant attitude similar to that of a kite to keep position stability and increase damping. Meanwhile, the winch only needs to recover the cable at a constant speed until the tethered lifting-wing multicopter returns to its base. Real flight experiments demonstrate the feasibility and practicability of this method.

As the usage of bi-copters increases in military and civilian fields, the demand for reliable bi-copters is on the rise. This study focuses on controlling a bi-copter under rotor or servo stuck failure. A relaxed hover solution is derived for the bi-copter, by solving an optimization problem subject to rotor and servo stuck failures. The solution is used for designing a reduced attitude controller based on linear quadratic regulator (LQR). To ensure hover capability, we introduce a position controller based on a cascaded-PID. The numerical simulations are conducted to demonstrate that position control is possible, even with complete rotor or servo stuck failure, by driving the bi-copter into relaxed hover state through the abandonment of the yaw channel. Meanwhile, the FTC scheme is examined under constant wind disturbances and uncertainties in the rotational damping parameters.

In V-SLAM, the estimation of relative camera pose is crucial to determine the spatial relationship between consecutive camera images, helping to accurately track the movement of the camera in its environment. In small indoor scenes, when the training set is limited, which is very common in robot SLAM, learning-based methods may fail to converge, especially the Transformer architecture, which requires a more substantial dataset to match the performance of the CNN architecture model. This work addresses this problem with the sharing attention mechanism, building on recent improvements in solving visual Transformer architectures on small datasets while incorporating messenger tokens. Besides, double-embedding is introduced to capture the spatial of images and order of images. In summary, we introduce an intuitive end-to-end relative pose estimation solution and prove its accuracy on the two smallest sub-datasets of 7Scenes. The proposed method is tested with a set of comparison experiments conducted across CNN-based, Transformer-based end-to-end relative pose estimation models, and the robust feature-matching non-learning method. Our model outperforms in all comparisons. Furthermore, ablation studies clearly illustrate that these innovations are crucial for the accuracy of relative pose estimation on small datasets.

Swarm robotics plays a non-negligible role in actual practice because of its scalability and robustness. Besides some specific studies, there is still a lack of overall approaches to solving the search and rescue problem in a communication-denied environment. This paper presents a bee-inspired swarm cooperation approach without information exchange, including a target grouping method suitable for multi-objective and multi-robot, a finite behavior state machine, and the corresponding control law. Finally, the effectiveness of the proposed approach is shown via simulation. The overall approach proposed in this paper does not require two-way information exchange, and it is robust against relative and own position errors, making swarm robotics search and rescue in a communication-denied environment possible.

It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness.

It is crucial for hybrid unmanned aerial vehicles, such as lifting-wing multicopters, to plan a continuous, smooth, and collision-free trajectory to avoid obstacles. Unlike quad-copters, which typically work in indoor environments, lifting-wing multicopters typically fly at a high altitude with a high cruising speed, requiring higher maneuverability in the vertical direction. Inspired by birds, lifting-wing multicopters can take an inverted flight maneuver to gain more maneuverability than the corresponding multicopter owing to the additional lifting wing. In this paper, a rotation-aware collision-free motion planning strategy is proposed that takes aerodynamics into consideration and allows lifting-wing multicopters to fly at large rotation angles, even in inverted postures. Specifically, a collision-free state sequence is found using rotation-aware primitives by solving a graph search problem. The sequence is then refined with B-spline into smooth trajectories to be tracked by the differential flatness-based controller for lifting-wing multicopters. We analyze the proposed motion planning algorithm in different scenarios and demonstrate the feasibility of the generated trajectories in simulation and real-world experiments. Video: https://youtu.be/n87jK81zg_I

Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task can reach 96.93% using OA-Bug, which is significantly improved compared with a similar algorithm, SGBA [1]. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment. Video: https://youtu.be/vj9cRiSmgeM.

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. Several types of airspace structures have been proposed in recent research, which include several structured free flight concepts. In this paper, for simplicity, distributed coordination of the motions of cooperative multicopters in structured airspace concepts is focused on. This is formulated as a free flight problem, including convergence to destination lines/planes and inter-agent collision avoidance. The destination line of each multicopter is known a priori. Further, Lyapunov-like functions are designed elaborately, while formal analysis and proofs of the proposed distributed control are given to show that the free flight control problem can be solved. What is more, by the proposed controller, a multicopter can keep away from another as soon as possible once it enters the safety area of another one. Simulations and experiments are given to show the effectiveness of the proposed method.

Unmanned aerial vehicles are now becoming increasingly accessible to amateur and commercial users alike. A safety air traffic management system is needed to help ensure that every newest entrant into the sky does not collide with others. Much research has been done to design various methods to perform collision avoidance with obstacles. However, how to decide the safety radius subject to communication uncertainties is still suspended. Based on assumptions on communication uncertainties and supposed control performance, a separation principle of the safety radius design and controller design is proposed. With it, the safety radius in the design phase (without uncertainties) and flight phase (subject to uncertainties) are studied. Furthermore, the results are extended to multiple obstacles. Simulations and experiments are carried out to show the effectiveness of the proposed methods.

This study proposes auniformpassivefault-tolerant control (FTC) method for a quadcopter that does not rely on fault information subject to one, two adjacent, two opposite, or three rotor failure. Theuniformcontrol implies that thepassiveFTC is able to cover the condition from quadcopter fault-free to rotor failure without the need for controller switching. To achieve the purpose ofpassiveFTC, the fault of rotors is modeled as a lumped disturbance acting on the virtual control of the quadcopter system. The estimated disturbance is used directly in thepassiveFTC. At the same time, a modified controller structure is designed to achieve the passive FTC ability for two and three rotor failure. To avoid the control allocation switching from the fault-free control to the FTC, a dynamic control allocation is used. In addition, the closed-loop stability is analyzed in the presence of up to three rotor failure. To validate the proposeduniformpassiveFTC method, outdoor experiments are performedfor the first time, which have demonstrated that the hovering quadcopter is able to recover from one rotor failure using the proposed controller and resume its mission even if two adjacent, two opposite, or three rotors fail, without the need for any rotor fault information or controller switching. Experimental results can be viewed in this video:https://youtu.be/N1OudPXFXnE. Source code is placed onhttps://github.com/RflyBUAA/DegradedControl.git

The success of deep learning methods relies on the availability of well-labeled large-scale datasets. However, for medical images, annotating such abundant training data often requires experienced radiologists and consumes their limited time. Few-shot learning is developed to alleviate this burden, which achieves competitive performances with only several labeled data. However, a crucial yet previously overlooked problem in few-shot learning is about the selection of template images for annotation before learning, which affects the final performance. We herein propose a novel Sample Choosing Policy (SCP) to select “the most worthy” images for annotation, in the context of few-shot medical landmark detection. SCP consists of three parts: 1) Self-supervised training for building a pre-trained deep model to extract features from radiological images, 2) Key Point Proposal for localizing informative patches, and 3) Representative Score Estimation for searching the most representative samples or templates. The advantage of SCP is demonstrated by various experiments on three widely-used public datasets. For one-shot medical landmark detection, its use reduces the mean radial errors on Cephalometric and HandXray datasets by 14.2% (from 3.595mm to 3.083mm) and 35.5% (4.114mm to 2.653mm), respectively.

This paper proposes a model of a class of regular virtual tubes that can generate safe, feasible, and smooth space for a robotics swarm in an obstacle-dense environment, especially for a drone swarm based on the flocking model. The regular principles are first proposed, and the regular conditions are then formulated based on the principles. A method to obtain a regular virtual tube is also presented based on trajectory planning and regular conditions. The proposed method's effectiveness and robustness are comprehensively demonstrated in a simulation environment with random obstacles.

The traditional multicopter design method usually requires a long iterative process to find the optimal design based on given performance requirements. The method is uneconomical and inefficient. In this article, a practical method is proposed to automatically calculate the optimal multicopter design according to the given design requirements including flight time, altitude, payload capacity, and maneuverability. The proposed method contains two algorithms, including an off-line algorithm and an online algorithm. The off-line algorithm finds the optimal components (propeller and electronic speed controller) for each motor to establish its component combination, and subsequently, these component combinations and their key performance parameters are stored in a combination database. The online algorithm obtains the multicopter design results that satisfy the given requirements by searching through the component combinations in the database and calculating the optimal parameters for the battery and airframe. Subsequently, these requirement-satisfied multicopter design results are obtained and sorted according to an objective function that contains evaluation indexes, including size, weight, performance, and practicability. The proposed method has the advantages of high precision and quick calculating speed because parameter calibrations and time-consuming calculations are completed offline. Experiments are performed to validate the effectiveness and practicality of the proposed method. Comparisons with the brutal search method and other design methods demonstrate the efficiency of the proposed method.Note to Practitioners—The proposed method is fast and practical to obtain an optimal solution by only using a low-performance web server, and the algorithm has been published online athttp://www.flyeval.com/recalc.htmlto provide an online optimization design service for users. To make it convenient to apply the proposed method to multicopter designs, the propulsion system combination database obtained by our off-line algorithm is released along with the article. This database includes more than 1500 experimentally calibrated propulsion combinations, which are adequate for readers to use the proposed optimization algorithms to design multicopters with weights (sizes) ranging from 0.2 to 50 kg.

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. The main task for UAVs is to keep a prescribed separation with obstacles in the air. In this paper, a collision-avoidance control method for non-cooperative moving obstacles is proposed for a multicopter with the altitude hold mode by using a Lyapunov-like barrier function. Lyapunov-like functions are designed elaborately, based on which formal analysis and proofs of the proposed control are made to show that the collision-avoidance control problem can be solved if the moving obstacle is slower than the multicopter. The result can be extended to some cases of multiple obstacles. What is more, by the proposed control, a multicopter can keep away from obstacles as soon as possible, once obstacles enter into the safety area of the multicopter accidentally, and converge to the waypoint. Simulations and experiments are given to show the effectiveness of the proposed method by showing the distance between UAV and waypoint, obstacles respectively.

This paper aims to present a drone swarm light show design platform to support STEAM (science, technology, engineering, art and mathematics) education for K-12 children. With this platform, children can use this platform to design a drone swarm light show easily. To this end, the architecture of this platform contents three layers: UI layer, command layer, and physical layer. The UI layer has an easy-to-use interface for children. Children can feed parameters about the light show by clicking buttons and dragging sliders of four tracks. All actions designed for the swarm in the UI layer will be generated automatically in the form of the drone’s desired trajectories through the command layer. The physical layer includes a router for communication and a drone swarm for the light show. Our experimental results demonstrate that this platform works efficiently and suits for being applied to real STEAM education.

For most people on the ground, facing an unwanted drone buzzing around overhead, there is not a lot that we can do, especially if it is out of gun (radio wave gun or shotgun) range. A solution to this is to use intercept drones that seek out and bring down other drones. In order to make the interception autonomous, an image-based visual servo algorithm is designed with a forward-looking monocular camera. The control command, namely the angular velocity and thrust, is generated for intercept drones to implement accurate and fast interception. The proposed method is demonstrated in both hardware-in-the-loop simulation and demonstrative flight experiments.

Relative pose estimation is critical for collaborative multi-agent systems. To achieve accurate and low-cost localization in cluttered and GPS-denied environments, we propose a novel relative pose estimation system based on a designed active infrared coded target. Specifically, each agent is equipped with a forward-looking monocular camera and a unique infrared coded target. The target with the unique lighted LED arrangement is detected by the camera and processed with an efficient decoding algorithm. The relative pose between the agent and the camera is estimated by combining a PnP algorithm and a Kalman filter. Various experiments are performed to show that the proposed pose estimation system is accurate, robust and efficient in cluttered and GPS-denied environments.

With the proliferation of AI-based applications and services, there are strong demands for efficient processing of deep neural networks (DNNs). DNNs are known to be both compute-and memory-intensive as they require a tremendous amount of computation and large memory space. Quantization is a popular technique to boost efficiency of DNNs by representing a number with fewer bits, hence reducing both computational strength and memory footprint. However, it is a difficult task to find an optimal number representation for a DNN due to a combinatorial explosion in feasible number representations with varying bit widths, which is only exacerbated by layer-wise optimization. Besides, existing quantization techniques often target a specific DNN framework and/or hardware platform, lacking portability across various execution environments. To address this, we propose libnumber, a portable, automatic quantization framework for DNNs. By introducing Number abstract data type (ADT), libnumber encapsulates the internal representation of a number from the user. Then the auto-tuner of libnumber finds a compact representation (type, bit width, and bias) for the number that minimizes the user-supplied objective function, while satisfying the accuracy constraint. Thus, libnumber effectively separates the concern of developing an effective DNN model from low-level optimization of number representation. Our evaluation using eleven DNN models on two DNN frameworks targeting an FPGA platform demonstrates over 8× (7×) reduction in the parameter size on average when up to 7% (1%) loss of relative accuracy is tolerable, with a maximum reduction of 16×, compared to the baseline using 32-bit floating-point numbers. This leads to an geomean speedup of 3.79× with a maximum speedup of 12.77× over the baseline, while requiring only minimal programmer effort.

In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-time, equality-constrained optimization is developed. First, the equality constraint is transformed into a continuous-time dynamical system with solutions that always satisfy the equality constraint. Second, a new projection matrix without singularity is proposed to realize the transformation. An update (or say a controller) is subsequently designed to decrease the objective function along the solutions of the transformed continuous-time dynamical system. The invariance principle is then applied to analyze the behavior of the solution. Furthermore, the proposed method is modified to address cases in which solutions do not satisfy the equality constraint. Finally, the proposed optimization approach is applied to three examples to demonstrate its effectiveness.

Fish‐eye cameras are becoming increasingly popular in computer vision, but their use for three‐dimensional measurement is limited partly because of the lack of an accurate, efficient and user‐friendly calibration procedure. For such a purpose, the authors propose a method to calibrate the intrinsic and extrinsic parameters (including radial distortion parameters) of two/multiple fish‐eye cameras simultaneously by using a wand under general motions. Thanks to the generic camera model used, the proposed calibration method is also suitable for two/multiple conventional cameras and mixed cameras (e.g. two conventional cameras and a fish‐eye camera). Simulation and real experiments demonstrate the effectiveness of the proposed method. Moreover, the authors develop the camera calibration toolbox, which is available online.

Prognostics and health management (PHM) technology has been widely accepted, and employed to evaluate system performance. In practice, system performance often varies continually rather than just being functional or failed, especially for a complex system. Profust reliability theory extends the traditional binary state space {0, 1} into a fuzzy state space [0, 1], which is therefore suitable to characterize a gradual physical degradation. Moreover, in profust reliability theory, fuzzy state transitions can also help to describe the health evolution of a component or a system. Accordingly, this paper proposes a profust reliability based PHM approach, where the profust reliability is employed as a health indicator to evaluate the real-time system performance. On the basis of the health estimation, the system remaining useful life (RUL) is further defined, and the mean RUL estimate is predicted by using a degraded Markov model. Finally, an experimental case study of Li-ion batteries is presented to demonstrate the effectiveness of the proposed approach.