The steerability of catheter is critical to the success of interventional procedure. In this paper, a magnetic continuum robot is presumably mounted to the distal of a catheter to pull it in the narrow, bifurcate, tortuous pathways of the blood vessels. The continuum robot is actuated by a permanent magnet. It is linear and soft, which complicates its interaction with the magnet. However, it generates the omnidirectional deflection at its tip and improves its steerability instead. In the non-uniform field generated by a permanent magnet, besides magnetic torque, magnetic force is acting on the robot as well, and both are used to derive the dynamic equations to govern the interaction between the robot and the magnet, in terms of the Euler-Bernoulli beam theory. An iterative algorithm to calculate the magnet pose is proposed to generate an optimal moving magnetic field and actuate the robot to follow the planned trajectory at its tip. A robot prototype is fabricated, and the experimental results show that this prototype can accurately track the planned trajectories in 2D space by the magnet manipulation with a robot arm.

In robotic flexible ureteroscopy, great care should be taken to prevent excessive collision force at the ureteroscope tip to avoid urinary tissue damage. However, the passive flexure of a flexible ureteroscope from reaction forces with the surrounding tissue introduces great uncertainties in force control. To address this, a probability-based force control method is proposed in this paper. A morphological wavelet-based statistic test is proposed to detect collision by identifying the change point of the probability distribution of force signal, which is measured by a fiber optical sensor at the ureteroscope tip. The force signal and its change points are applied as an admittance model inputs to generate position command. It is augmented to the robot controller for minimizing the collision force. Meanwhile, a probabilistic model approximates the real interactive system, and combines Bayesian inference for safely online learning the admittance parameters. Experimental results on a flexible ureteroscopy robot show that collision can be detected instantly, and the force is reduced remarkably during the advancement of the robotic ureteroscope. Note to Practitioners—This paper is motivated by the rapidly increasing applications for robotic flexible ureteroscopy. As ureteroscope enters into urethra, urinary bladder, ureter, and reaches renal pelvis, it sometimes penetrates soft tissue, especially in the case of no ureteral access sheath placement. It is important to control the ureteroscope tip force for surgery safety. Therefore, a force control method is proposed in this paper. Experiments conducted on a flexible ureteroscopy robot demonstrate that the proposed method is a protective measure to mitigate the risk of inner surface perforation.

Point cloud-based 3D object detection technology provides precise information for detecting obstacles in front of trains. In rail transit scenarios, obstacles are usually located at a distance, and the sparsity of point cloud data leads to information loss during feature extraction and spatial transformation, adversely affecting the accuracy of obstacle detection. To tackle this issue, we propose RAE3D, a single-stage end-to-end architecture that incorporates a multiscale voxel feature aggregation module on point queries (MVA-PQ) and a bird's-eye view multilevel auxiliary (BEV-MLA) module for efficient 3D object detection. The MVA-PQ module encodes multiscale voxel features into centroids of the final voxel layer, integrating spatial and scale information. The BEV-MLA module converts shallow 3D features into sparse 2D features using height compression, guiding the network to learn the spatial structure of small objects. We also introduce a 3D centroid offset loss for optimizing bounding boxes. Extensive experiments on the Rail3D and KITTI datasets have demonstrated the superiority of the proposed RAE3D.

A simple yet effective semi-supervised method is proposed in this paper based on consistency regularization for crowd counting, and a hybrid perturbation strategy is used to generate strong, diverse perturbations, and enhance unlabeled images information mining. The conventional CNN-based counting methods are sensitive to texture perturbation and imperceptible noises raised by adversarial attack, therefore, the hybrid strategy is proposed to combine a spatial texture transformation and an adversarial perturbation module to perturb the unlabeled data in the semantic and non-semantic spaces, respectively. Moreover, a cross-distribution normalization technique is introduced to address the model optimization failure caused by BN layer in the strong perturbation, and to stabilize the optimization of the learning model. Extensive experiments have been conducted on the datasets of ShanghaiTech, UCF-QNRF, NWPU-Crowd, and JHU-Crowd++. The results demonstrate that the proposed semi-supervised counting method performs better over the state-of-the-art methods, and it shows better robustness to various perturbations.

Category-level 6D object pose estimation aims to estimate the pose and size of unseen objects with known categories. Existing methods mainly focus on capturing geometric features to handle shape variations, and are prone to failure in occlusion and noisy environments. In this paper, we propose TG-Pose, a unified pose estimation framework that delves into topology and geometry to deal with the above issues. To exploit topological properties, we first propose a topological feature predictor and a topological label generator to dig into the underlying structural details from encoded features using persistent homology. Then, the topological and geometric features are employed to facilitate the symmetry reconstruction of the original point cloud to obtain a reliable and coherent object shape, which, in turn, guides the pose estimation. For each object category, we construct geometric and topological templates by leveraging inherent intra-class similarities. These templates enhance the reliability of pose estimation and the completeness of object structure through geometric alignment and topological guidance, especially when handling incomplete objects. Moreover, a pose-aware enhancement strategy is designed to enhance the encoder in learning pose-sensitive features and robustness to noisy point clouds. Experimental results show that TG-Pose outperforms the state-of-the-art solutions on public benchmarks and achieves better generalization in real-world datasets. Project Page https://sites.google.com/view/tg-pose.

Semi-supervised crowd counting (SSCC) aims to learn a crowd counting model with limited labeled images and a large number of unlabeled images. Previous works leverage unlabeled images by pseudo-labeling and spatial consistency regularization paradigms, which frequently adopt teacher-student frameworks. However, their performances are readily degraded due to the inconsistent and unreliable pseudo density map in complex crowd scenes. Here, we argue that the SSCC performance can be significantly improved by reducing the over-fitting of the incorrect pseudo labels, and a novel spatial-temporal consistency framework, named STC-Crowd, is proposed. Under different spatial perturbations, spatial consistency enables the counting model to output consistent predictions for the same crowd image. Temporal consistency generates similar feature embedding over adjacent training stages, alleviating the inconsistent issues of pseudo density maps generated for the same image over time. To store the temporal feature embedding of different density levels for temporal consistency, a dynamic temporal knowledge memory (DTKM) is deliberately designed, and considerably reduces the storage cost. Besides, a pseudo-label filter (PLF) mechanism is used to alleviate the negative impact of incorrect pseudo density maps, by reducing the supervision weights of unreliable pseudo labels with high uncertainty. Extensive experiments on four benchmark datasets show that our method obtains competent performance against leading SSCC methods, and especially works better on limited labeled images.

This article investigates the tracking-oriented robust leaderless time-varying formation (TVF) control problem for unmanned aerial vehicle swarm systems (UAVSSs) with Lipschitz nonlinear dynamics under directed topology, where external disturbances are random and bounded, and communication delays (CDs) are bounded. In this article, a state-feedback control approach is adopted to make sure that a UAVSS forms a desired TVF and follows a specified trajectory when CDs and external disturbances occur. First, a novel PD-like formation control protocol with several unknown parameters and CDs is designed. The protocol contains the information of the local neighborhood status and its differential quantities. Second, the tracking-oriented robust leaderless TVF control problem with Lipschitz dynamics, external disturbances, and CDs is transformed into a problem about asymptotic stability of a lower dimensional closed-loop control system through a special matrix decomposition. Third, a theorem is proposed to determine the unknown parameters of the control protocol and the upper bound of CDs. In the theorem, sufficient conditions for a UAVSS to attain the anticipated TVF and trajectory tracking are obtained. A Lyapunov-Krasovskii (LK) functional is constructed to verify that the error among the practical flight state of UAVs, the anticipant TVF configuration, and tracking trajectory can asymptotically converge to 0. Finally, with the presentation of a simulation case, the effectiveness of the theoretical results is illustrated.

Snow detection and removal from video images is very challenging. Normally the snowflakes affect only on a very small region of an image, hence the confusion to determine which region should be considered and which one should not. In this paper, a frame difference method with five successive frames is first presented to detect the snow pixels from image background, but the method didn't work well in the case of heavy snow. Then a new technique has been implemented which uses the L0gradient minimization approach to remove the snow pixels. This technique can control how many non-zero gradients are resulted in the image, and is independent of local features, but instead locates important edges globally. These salient edges are preserved and the low amplitude and insignificant details are diminished. The snow pixels are then removed in this way. Experimental results show that this method is a highly efficient algorithm even under heavy snow conditions, while preserving the details of the image.