This article investigates the discontinuous adaptive impulsive control of uncertain linear and nonlinear systems with stochastic perturbations and actuator saturation. Existing literature on adaptive impulsive control schemes adopt continuous state information in designing the continuous adaptive law, which loses the advantages of impulsive control completely. In this article, the discontinuous adaptive law is proposed which only requires the state information be transmitted at impulsive instants, therefore, the communication cost could be reduced and the control system is more practical in implementation. Furthermore, a discontinuous adaptive impulsive control law is derived to realize stabilization of uncertain nonlinear systems with stochastic perturbations and actuator saturation, and the robustness of the closed-loop system with the discontinuous adaptive impulsive control scheme is proved to be effective. Finally, two simulation examples for adaptive impulsive control are presented to verify the accuracy of our results.

In this article, we propose a Swin Transformer and multilevel Feature Consistency based Network (STFC-Net), which is a multilevel cascade stereo matching method to predict the disparity in a coarse-to-fine manner. 1) To alleviate the problem of the limited receptive field of existing convolutional neural network (CNN)-based methods, inspired by the capability of modeling the large-scale dependence of transformer, we adopt a multilevel feature extraction module combining CNN and Swin Transformer to capture long-range context information; a multiscale cascaded cost aggregation module is used to cover different image regions with less memory consumption. 2) To make full use of the hierarchical features, we checked the multilevel left-right feature consistency in an unsupervised manner to improve the disparity accuracy. The experimental results show that our method outperforms some previous CNN methods on the Scene Flow and KITTI datasets with lower computational time complexity. Moreover, it generalizes well in some unknown and challenging real-world scenarios.

Multi-fisheye System has the advantages of sufficient overlap and the ability to capture a complete 360° scene, which is beneficial for the omnidirectional depth estimation task. However, due to the severe distortion of the fisheye images, it is hard for such systems to extract and match features to predict an accurate depth. In this work, on the basis of a multi-fisheye system, we present a novel end-to-end deep learning architecture for omnidirectional depth estimation: 1) to capture the reliable features of the distorted fisheye image, a multi-scale feature extraction and aggregation module is improved, which can adaptively obtain the global context information to represent the features; 2) to leverage more aligned features, especially those in the overlap between multi-fisheye images, we construct a fusion cost volume to combine similarity and semantic information, which can enhance the feature discriminability; and 3) to refine the omnidirectional depth map efficiently, a cascaded cost regularization architecture is proposed. Instead of several costly 3D convolutions, the 3D BSConv based on intra-kernel correlations is introduced to regularize the cost. The proposed method can incrementally predict the depth map from coarse to fine, and reduce the network computational complexity significantly. The experiments in several public indoor and outdoor synthetic datasets demonstrate that the proposed method outperforms some state-of-the-art methods in terms of a synthesis of accuracy and speed, with fewer model parameters.

By relabeling past experience with heuristic or curriculum goals, state-of-the-art reinforcement learning (RL) algorithms such as hindsight experience replay (HER), hindsight goal generation (HGG), and graph-based HGG (G-HGG) have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HGG outperforms HER in challenging tasks in which goals are difficult to explore by learning from a curriculum, in which intermediate goals are selected based on the Euclidean distance to target goals. G-HGG enhances HGG by selecting intermediate goals from a precomputed graph representation of the environment, which enables its applicability in an environment with stationary obstacles. However, G-HGG is not applicable to manipulation tasks with dynamic obstacles, since its graph representation is only valid in static scenarios and fails to provide any correct information to guide the exploration. In this article, we propose bounding-box-based HGG (Bbox-HGG), an extension of G-HGG selecting hindsight goals with the help of image observations of the environment, which makes it applicable to tasks with dynamic obstacles. We evaluate Bbox-HGG on four challenging manipulation tasks, where significant enhancements in both sample efficiency and overall success rate are shown over state-of-the-art algorithms. The videos can be viewed at https://videoviewsite.wixsite.com/bbhgg.

Reinforcement learning algorithms, such as hindsight experience replay (HER) and hindsight goal generation (HGG), have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HER achieves its training success through hindsight replays of past experience with heuristic goals but underperforms in challenging tasks in which goals are difficult to explore. HGG enhances HER by selecting intermediate goals that are easy to achieve in the short term and promising to lead to target goals in the long term. This guided exploration makes HGG applicable to tasks in which target goals are far away from the object's initial position. However, the vanilla HGG is not applicable to manipulation tasks with obstacles because the Euclidean metric used for HGG is not an accurate distance metric in such an environment. Although, with the guidance of a handcrafted distance grid, grid-based HGG can solve manipulation tasks with obstacles, a more feasible method that can solve such tasks automatically is still in demand. In this article, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment. We evaluated G-HGG on four challenging manipulation tasks with obstacles, where significant enhancements in both sample efficiency and overall success rate are shown over HGG and HER. Videos can be viewed at https://videoviewsite.wixsite.com/ghgg.

This paper presents a sliding mode dynamic output feedback controller design for Markovian jump systems under a communication network. For the inaccessible states and uncertain parts of Markovian jump systems, a novel integral-type sliding mode output feedback controller combined with system states and dynamic controller states is proposed. An event-triggered mechanism is introduced to reduce network bandwidth and resources requirements. The augmented dynamic system and complete control framework are presented together. The reachability of the novel integral-type sliding mode is proved. Then, based on delay-dependent Lyapunov functions, free-weighting matrices, and a singular value decomposition approach, a less conservative sufficient condition is proposed to ensure exponential stability with a weighted$\mathcal {L}_{2}$-$\mathcal{ L}_{\infty}$disturbance performance. Finally, a numerical and a DC-DC switched boost converter circuit simulations that verify the feasibility and effectiveness of the proposed controller are presented.

Accurate localization and tracking are a fundamental requirement for robotic applications. Localization systems like GPS, optical tracking, simultaneous localization and mapping (SLAM) are used for daily life activities, research, and commercial applications. Ultra-wideband (UWB) technology provides another venue to accurately locate devices both indoors and outdoors. In this paper, we study a localization solution with a single UWB anchor, instead of the traditional multi-anchor setup. Besides the challenge of a single UWB ranging source, the only other sensor we require is a low-cost 9 DoF inertial measurement unit (IMU). Under such a configuration, we propose continuous monitoring of UWB range changes to estimate the robot speed when moving on a line. Combining speed estimation with orientation estimation from the IMU sensor, the system becomes temporally observable. We use an Extended Kalman Filter (EKF) to estimate the pose of a robot. With our solution, we can effectively correct the accumulated error and maintain accurate tracking of a moving robot.

Automated assembly, especially peg-in-hole insertion, is a common task in manufacturing. In particular, the high-precision assembly is achieved by high-precision manipulator and sensing system. However, uncertainty and various parts for assembly are still challenges for robotic assembly, especially for low-precision robot and sensors. It is noteworthy that human can implement assembly tasks although the precision of the arm and hand is not comparable with a common industrial robot, in which process compliance is the key characteristic of their motion. In this paper, we present a human-inspired compliant strategy for peg-in-hole assembly task using the environmental constraint and coarse force information. In the proposed strategy, a constraint region is designed for motion planning and utilized for eliminating the uncertainty of the initial positioning error of the peg. Force sensor is applied to sense the contact force of which the direction is used to adjust the movement of the peg. Therefore, high-precision sensor is not necessarily required. Inspired by human compliant assembly, a from coarse to fine adjustment strategy is executed. The contribution of our strategy is that high precision assembly task can be solved by low precision system. The constraint region and force guided directional adjustment have increased the robustness of the system. The strategy is carried out in simulation for round peg-in-hole assembly task. The experimental results show that the assembly task can be successfully completed and demonstrate the effectiveness of our strategy.