Recent years have witnessed the remarkable progress of 3D multi-modality object detection methods based on the Bird's-Eye-View (BEV) perspective. However, most of them overlook the complementary interaction and guidance between LiDAR and camera. In this work, we propose a novel multi-modality 3D objection detection method, with multi-guided global interaction and LiDAR-guided adaptive fusion, named MGAF. Specifically, we introduce sparse depth guidance (SDG) and LiDAR occupancy guidance (LOG) to generate 3D features with sufficient depth and spatial information. The designed semantic segmentation network captures category and orientation prior information for raw point clouds. In the following, an Adaptive Fusion Dual Transformer (AFDT) is developed to adaptively enhance the interaction of different modal BEV features from both global and bidirectional perspectives. Meanwhile, additional downsampling with sparse height compression and multi-scale dual-path transformer (MSDPT) are designed in order to enlarge the receptive fields of different modal features. Finally, a temporal fusion module is introduced to aggregate features from previous frames. Notably, the proposed AFDT is general, which also shows superior performance on other models. Our framework has undergone extensive experimentation on the large-scale nuScenes dataset, Waymo Open Dataset, and long-range Argoverse2 dataset, consistently demonstrating state-of-the-art performance.

In robot-assisted bending operations, the confined space between the upper and lower dies of the machine, combined with the dynamic shape alterations of the workpiece, presents a significant risk of collisions. Conventional Rapidly-exploring Random Tree (RRT) algorithms exhibit limitations in terms of slow convergence and suboptimal path quality under such conditions. Additionally, many existing approaches prioritize path efficiency without adequately considering path safety. To address these limitations, a hierarchical path planning method is proposed. First, we use an enhanced RRT algorithm, called improved synchronized-biased greedy RRT-Connect (SBG-RRT-Connect), to generate initial paths more efficiently. This algorithm improves convergence and avoids unproductive areas, speeding up the process and reducing search time by 67.05% and iterations by 76.06%. Next, we introduce a safety measure based on the Swept Volume Signed Distance Field (SVSDF) of the workpiece’s motion, which helps identify potential collision risks. Multi-objective optimization is achieved using the Water Cycle Algorithm (WCA), enabling a balance between path cost and safe distance from obstacles. Experimental results verify the method’s applicability to workpieces with varying complexities. The path effectiveness is further validated through a digital twin-based simulation platform and physical implementation.

Regular maintenance and inspection of cables are essential for cable-stayed bridges and suspension bridges, as cables serve as the core components. In order to enable automated detection of cables, this paper proposes a novel wheeled cable inspection robot. The robot utilizes a bilateral wheel structure and is composed of four independent suspension mechanisms. By collaborating with a lifting mechanism, the robot achieves functions such as adhesion, climbing, and obstacle overcoming. The robot is powered by a lithium polymer battery and operated via wireless control by ground personnel. This paper provides a detailed exposition on the structure design and control system of robots, and conducts a mechanical analysis of the suspension mechanism of robots. The maximum obstacle-negotiation height of the robot is calculated, and a mechanical model for cable climbing is established. During prototype testing, the robot demonstrated a mass of 6.7kg, a maximum payload capacity of 6kg, a maximum obstacle height of 10mm, and a fastest climbing speed of 14m/min. These specifications meet the requirements of practical inspections.

Real neural networks are characterized by large-scale and complex topology. However, the current dynamical analysis is limited to low-dimensional models with simplified topology. Therefore, there is still a huge gap between neural network theory and its application. This article proposes a class of large-scale neural networks with a ring-hub structure, where a hub node is connected to n peripheral nodes and these peripheral nodes are linked by a ring. In particular, there exists a hybrid coupling mode in the network topology. The mathematical model of such systems is described by fractional-order delayed differential equations. The aim of this article is to investigate the local stability and Hopf bifurcation of this high-dimensional neural network. First, the Coates flow graph is employed to obtain the characteristic equation of the linearized high-dimensional neural network model, which is a transcendental equation including multiple exponential items. Then, the sufficient conditions ensuring the stability of equilibrium and the existence of Hopf bifurcation are achieved by taking time delay as a bifurcation parameter. Finally, some numerical examples are given to support the theoretical results. It is revealed that the increasing time delay can effectively induce the occurrence of periodic oscillation. Moreover, the fractional order, the self-feedback coefficient, and the number of neurons also have effects on the onset of Hopf bifurcation.

This paper designed a pneumatic soft climbing robot by utilizing the high flexibility of soft materials. Capabilities of climbing and creeping through small spaces would rarely possible with a method based only on rigid links. At first, according to the drive mode of pneumatic networks, a model for soft climbing robots with different section having their independent stiffness was designed; afterwards, the analysis of visco-mechanical properties of robots at the contact surface were provided by using the method of minimum potential energy; on the basis, finite element analysis and experiment are given to analyze the climbing behaviors of the robot; finally, by employing 3-d printing and layer-by-layer casting, a prototype soft climbing robot was prepared to perform climbing experiments. The research is expected to provide a new method for monitoring complex unstructured environments.