The two-step floating catchment area (2SFCA) model and its variants have been widely employed in spatial accessibility research. However, these models typically rely on predefined supply, demand, and travel impedance data and produce deterministic outputs, making them primarily suited for accessibility assessment rather than supporting optimization. To address this limitation, this study introduces the cost-balanced accessibility (CBA) model, an advanced framework built upon the congestion supply accessibility (CSA) model. Theoretically, the CBA model incorporates an iterative approach to eliminate instability in congestion calculations in the CSA model and establishes convergence conditions for the iterative process. Furthermore, the CBA model introduces an additional parameter to reconcile the scaling differences between travel cost and supply capacity, avoiding counterintuitive outcomes such as ‘preferring distant facilities over nearby ones’ in the CSA model. The CBA model also utilizes Pareto optimization to identify optimal parameters, enabling simulation of supply-demand allocation under various urban management objectives, such as maintaining average congestion or travel costs below predefined thresholds. Validated with both simulated and real-world data, the CBA model integrates multi-objective optimization into the 2SFCA framework for the first time, offering a versatile tool to adjust supply–demand balance and mobility under diverse urban management objectives.

In robotic operation, asymmetric tasks requiring dual-arm cooperation are the highly challenging research direction. Autonomous operation generally has a low success rate or poor generalization because of its excessive dependence on the accuracy of sub-goals from asymmetric tasks. Although teleoperation can significantly improve the performances above, during operation, the operators are prone to neglect crucial intermediate sub-goals that are conducive to fine-grained dual-arm cooperation. Therefore, we propose a dual-arm shared control framework which firstly introduces the Sub-goal Generation module to sufficiently concentrate on the intermediate states, thus improving the ability of fine-grained dual-arm cooperation and reducing the adjustment quantity during robotic asymmetric task operation. Also, we integrate the Trajectory Prediction module that computes the future trajectory based on the historical movement information to enhance the robot motion smoothness. Finally, through dynamic combination of Sub-goal Generation module, Trajectory Prediction module and operator movement in the shared control framework, we effectively decrease the sensitivity to the accuracy of sub-goals, thus significantly improving the success rate. In simulation, we conduct the comparative experiments with autonomous operation and teleoperation on four common asymmetric tasks to validate the advantages of our shared control framework. The effect of each element in our framework is verified by ablation study. Certainly, our shared control framework can also be applied in real-world scenario.

Near-surface air temperature (Ta2M), defined as the temperature at 2 m above the ground, is influenced by various dynamical, radiative, and surface-atmosphere exchange processes. Despite numerous methods are developed to estimate daily or monthly mean temperatures, high spatiotemporal resolution products remain scarce. Longwave radiation, land surface temperature (LST), and total column water vapor (TCWV) are considered driving factors for retrieving Ta2M. This study proposes a new method that integrates the strengths of multiple products to generate a balanced global hourly averaged Ta2M dataset. Initially, this study proposes a reconstruction approach to derive longwave downward radiation (LWDR) based on random forest regression (RFR) by combining remote sensing and reanalysis datasets. Furthermore, a multiple linear regression approach is employed to model the interactions among Ta2M, longwave radiation, LST, and TCWV, resulting in a global long-term Ta2M product characterized by a spatial resolution of 0.05° and an hourly temporal resolution. The produced dataset exhibits reasonable accuracy, with root mean square error (RMSE) less than 3.1 K and bias less than 0.4 K. The method proposed in this study offers innovative strategies for estimating Ta2M from remote sensing and reanalysis datasets. It provides valuable insights into the dynamics of radiative and surface-atmosphere exchange processes, contributing to a more comprehensive understanding of Earth’s climate system.

In the Vision-and-Language Navigation (VLN) task, an agent must comprehend natural language instructions and execute precise navigation in complex environments. While significant progress has been made in the VLN field, the limited availability of navigation data hinders existing methods from fully learning the commonsense relationships between rooms and landmarks, which are crucial for environmental understanding and successful navigation. To address this issue, this work proposes a Visual and Textual Commonsense-Enhanced Layout Learning Model (ViTeC). We leverage the open-world knowledge embedded in large models by utilizing ChatGPT and BLIP-2 to provide commonsense information about environments. Specifically, BLIP-2 analyzes the room type corresponding to each panoramic image, while ChatGPT infers and provides knowledge about the most common landmarks within each room type. Moreover, to compensate for the agent’s lack of commonsense at the visual level, we employ Stable Diffusion to generate commonsense-based visual images, enhancing the agent’s visual perception. To ensure the agent effectively learns commonsense about the environment, we designed a Text Commonsense Layout Learning Module and a Visual Commonsense Layout Learning Module. These modules help the agent acquire environmental commonsense from both linguistic and visual perspectives, enabling it to utilize commonsense information effectively during navigation, thereby improving its environmental understanding and reasoning capabilities. Experimental results demonstrate that ViTeC achieves strong performance on REVERIE, R2R, and SOON datasets, exhibiting good generalization ability in complex environments. This validates the effectiveness of ViTeC in enhancing the agent’s environmental understanding and navigation capabilities.

In robot-assisted live-line maintenance, bilateral teleoperation is still a popular and effective approach in assisting operators to accomplish hazards tasks. Particularly, teleoperation under overhead power lines attach greater expectation on safe operation and telepresence. In this article, we propose a visual-haptic bilateral teleoperation strategy, i.e.,EMGP-VH, based on visual guidance, haptic constraint and mixed reality (MR) augmentation. To the best of our knowledge, electromagnetic field is first applied to serve the path planning of teleoperation in live-line maintenance. In visual guidance, EMG-potential fields are integrated intoRRT*to calculate a low-energy path. At the same time, real-time haptic constraint is calculated based on a tube virtual fixture. MR augmentation also works as an indispensable part in both the platform construction and visual guidance. Our proposal has been extensively compared using seven objective performances and three subjective questionnaires both in simulation and real-world experiment with five different scenes and two approaches state-of-the-art, respectively. Functionality ofEMGP-RRT*and effectiveness of haptic constraint are further analyzed. Results show thatEMGP-RRT*has significant improvements both in searching efficiency and safety performances; and the proposed system (EMGP-VH) significantly contributes to improving telepresence and ensuring safe operations during live-line maintenance, resulting in a 30% reduction in operation time and a 60% decrease in trajectory offset.

3D hand pose estimation can be achieved by cascading a feature extraction module and a feature exploitation module, where reinforcement learning (RL) is proved to be an effective way to perform feature exploitation. This paper points out the prospects of improving accuracy using better exploitation strategy, and proposes an Adaptive Step-Critic Shared RL (ASCS-RL) strategy for accurate feature exploitation in 3D hand pose estimation. Hand joint features are exploited in a multi-task manner, and divided into two groups according to the distributions of estimation error. An RL-based adaptive-step (AS-RL) strategy is then used to obtain the optimal step size for better exploitation. The exploitation process are finally performed using a critic-shared RL (CS-RL) strategy, where both groups share a universal critic mechanism. Ablation studies and extensive experiments are carried out to evaluate the performance of ASCS-RL on ICVL and NYU datasets. The results show the strategy achieves the state-of-the-art accuracy in monocular depth-based 3D hand pose estimation, especially the best on ICVL. Experiments also validates that ASCS-RL realizes better tradeoff between accuracy and running rapidity.

For many vision tasks and intelligent robotics applications, it is common that the scanned 3D point cloud is not complete, so inferring from the residual defect shape to the intact shape becomes an essential task. Previous 3D completion neural network models generally use voxel-based or point-based methods to learn and process 3D data. For the voxel-based models, the computational cost and memory increase exponentially with the improvement of input resolution, and fine-grained features cannot be guaranteed in the completed point cloud due to limited computational resources . Point-based models suffer from the lack of precision in feature acquisition and crude reconstruction of complicated structures, making it extremely hard to accomplish elaborated semantic shapes. Combining advantages of voxel-based and point-based feature extraction through the high-frequency feature fusion module, this paper proposes a dual-scale point cloud completion network called DSNet, which performs global feature analysis at the voxel scale, and local feature analysis at the point cloud scale. The fused features are then integrated into the decoding and generation process, so as to complete the point cloud completion task from coarse to fine. Experimental results, at both quantitative and qualitative perspectives, in several prevailing datasets demonstrate that our approach surpasses state-of-the-art point cloud completion networks and has a good generalization performance . Code is available at https://github.com/engqing/DSNet.

Abstract 6D pose estimation has been pervasively applied to various robotic applications, such as service robots, collaborative robots, and unmanned warehouses. However, accurate 6D pose estimation is still a challenge problem due to the complexity of application scenarios caused by illumination changes, occlusion and even truncation between objects, and additional refinement is required for accurate 6D object pose estimation in prior work. Aiming at the efficiency and accuracy of 6D object pose estimation in these complex scenes, this paper presents a novel end‐to‐end network, which effectively utilises the contextual information within a neighbourhood region of each pixel to estimate the 6D object pose from RGB‐D images. Specifically, our network first applies the attention mechanism to extract effective pixel‐wise dense multimodal features, which are then expanded to multi‐scale dense features by integrating pixel‐wise features at different scales for pose estimation. The proposed method is evaluated extensively on the LineMOD and YCB‐Video datasets, and the experimental results show that the proposed method is superior to several state‐of‐the‐art baselines in terms of average point distance and average closest point distance.

In this paper, we propose an efficient Multi-Circle detector which follows the fixed search order. The method makes use of horizontal and vertical search to realize circle detection, which is named as HVCD. First, this method computes edge areas in a given image. The edge areas could be divided into some regions by means of region growing. Each of regions could be efficiently searched to achieve not only one-pixel wide edges but edge segments as well. Next, the candidate circles can be extracted from every edge segment. Finally, the circle candidates could be validated with the help of Helmholtz principle. Experimental results demonstrate that HVCD could effectively detect circles on synthetic and natural images on the one hand; on the other hand, HVCD here could solve the weakness in the process of circle Hough transform implementation and EDcircles implementation.

Coordination is playing a key role in complex cyber-physicalsystems (CPSs). The complexity and importance of coordination models and languages for CPSs necessarily lead to a higher relevance of testing during development of CPSs. Model-based testing is a promising technology to test the conformance or non-conformance relation between the implementation-under-test (IUT) and its specification. In this paper, we present an approach to test the conformance relation tiococ(Timed Input-Output Conformance) between the implementation of a timed Reo connector and its specification given by a timed constraint automaton (TCA). An algorithm to generate test cases from a TCA is proposed and the testing approach is implemented in UPPAAL.