In autonomous exploration, a robot navigates itself in an unknown environment while building a 2D map of the environment. This is typically done using a LiDAR sensor, which however is susceptible to error accumulation. To handle this issue, a UWB/LiDAR fusion SLAM is proposed, which can be decoupled into a localization problem and a mapping problem. For localization problem, we firstly apply extended Kalman filter (EKF) to localize all UWB beacons and then use particle filter (PF) to estimate the robot’s state based on the two on-board UWB nodes’ estimated locations. For mapping problem, we firstly fine-tune the robot’s state using a recursive adaptive-trust-region scan matcher, which is termed as RASM, and then construct the map based on the refined robot’s state. We also propose a method to correct UWB beacons’ locations using the robot’s refined location. Furthermore, the information obtained from the proposed fusion SLAM is utilized to sketch the region where the robot is going to explore next. That is, a where-to-explore strategy is proposed to guide the robot to the less-explored areas. Overall, the proposed exploration system is infrastructure-less and avoid mapping error to accumulate over time. Extensive experiments with comparisons to the state-of-the-art methods are conducted in two different environments: a cluttered workshop and a spacious garden in order to verify the effectiveness of our proposed strategy. The experimental tests are filmed and the video is available in the supplementary materials.

The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains. Ultra Wide Band (UWB) technology has emerged as a promising candidate for addressing this need, offering high precision, immunity to multipath interference, and robust performance in challenging environments. In this comprehensive survey, we systematically explore UWB-based localization for mobile autonomous machines, spanning from fundamental principles to future trends. To the best of our knowledge, this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines, covering a spectrum from bottom-ranging schemes to advanced sensor fusion, error mitigation, and optimization techniques. By synthesizing existing knowledge, evaluating current methodologies, and highlighting future trends, this review aims to catalyze progress and innovation in the field, unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains. Thus, it serves as a valuable resource for researchers, practitioners, and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.

On state estimation problems of switched neural networks, most existing results with an event-triggered scheme (ETS) not only ignore the estimator information, but also just employ a fixed triggering threshold, and the estimation error cannot be guaranteed to converge to zero. In addition, the state estimator of non-switched neural networks with integral and exponentially convergent terms cannot be used to improve the estimation performance of switched neural networks due to the difficulties caused by the nonsmoothness of the considered Lyapunov function at the switching instants. In this paper, we aim at overcoming such difficulties and filling in the gaps, by proposing a novel adaptive ETS (AETS) to design an event-based H_∞ switched proportional–integral (PI) state estimator. A triggering-dependent exponential convergence term and an integral term are introduced into the switched PI state estimator. The relationship among the average dwell time, the AETS and the PI state estimator are established by the triggering-dependent exponential convergence term such that estimation error asymptotically converges to zero with H_∞ performance level. It is shown that the convergence rate of the resultant error system can be adaptively adjusted according to triggering signals. Finally, the validity of the proposed theoretical results is verified through two illustrative examples.

In this paper we address the issue of output-feedback robust control for a class of feedforward nonlinear systems. Essentially different from the related literature, the feedback/input signals are corrupted by additive noises and can only be transmitted intermittently due to the consideration of event-triggered communications, which bring new challenges to the control design. With the aid of matrix pencil based design procedures, regulating the output to near zero is globally solved by a non-conservative dynamic low-gain controller which requires only an a priori information on the upper-bound of the growth rate of nonlinearities. Theoretical analysis shows that the closed-loop system is input-to-state stable with respect to the sampled errors and additive noise. In particular, the observer and controller designs have a dual architecture with a single dynamic scaling parameter whose update law can be obtained by calculating the generalized eigenvalues of matrix pencils offline, which has an advantage in the sense of improving the system convergence rate.