As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments.

Maximizing the fundamental eigenfrequency is an efficient means for vibrating structures to avoid resonance and noises. In this study, we develop an isogeometric analysis (IGA)-based level set model for the formulation and solution of topology optimization in cases with maximum eigenfrequency. The proposed method is based on a combination of level set method and IGA technique, which uses the non-uniform rational B-spline (NURBS), description of geometry, to perform analysis. The same NURBS is used for geometry representation, but also for IGA-based dynamic analysis and parameterization of the level set surface, that is, the level set function. The method is applied to topology optimization problems of maximizing the fundamental eigenfrequency for a given amount of material. A modal track method, that monitors a single target eigenmode is employed to prevent the exchange of eigenmode order number in eigenfrequency optimization. The validity and efficiency of the proposed method are illustrated by benchmark examples.

In large-scale structured scenes, efficient and reliable global localization is hard to achieve for mobile robots due to the substantial increase in the search space. This article addresses this issue by proposing an optimized branch and bound (BnB) based global localization using multiscale/resolution maps, which can efficiently find the global optimal initial pose with a high success rate. Multiscale feature maps (MSFMs) are first constructed to reflect the explicit point/line information of the structured scene. Then, to narrow the position space, robust features are extracted from LiDAR data at different scales and matched with MSFMs instantaneously using the hash function. Utilizing the attitude and heading reference system, a yaw angle estimation considering environmental interference is achieved to reduce the orientation space and improve the localization reliability in ambiguous environments. Besides, with the optimal resolution level and sparse point cloud sampled from interval filtering, an optimized BnB-based search is presented by using multiresolution maps. Finally, the effectiveness of the proposed method is verified by mobile robot real-world and simulated experiments in large-scale structured environments.

This article focuses on the fast finite-time tracking consensus for uncertain nonlinear multiagent systems. By gracefully combining the hierarchical decomposition, adding a barrier power integrator and disturbance compensation techniques, a new adaptive fast finite-time controller is proposed. Compared with the existing works, the proposed method has several distinguishing features: 1) It can achieve fast finite-time convergence with full state constraints; 2) it can deal with some fully unknown nonlinearities and state-dependent disturbance. Moreover, the nonlinearities can be related with the states of all the followers; 3) the input of the leader is not required to be zero, meaning a broad class of references signals can be generated; and 4) the controller is computationally simply and ready to be implemented. No fuzzy logic/neural networks are needed. Applications of the proposed method on multiple servo motors are also studied.

This paper aims to develop a non-cascade speed control approach for permanent magnet synchronous motor (PMSM) systems based on the Takagi–Sugeno (T–S) fuzzy model. In non-cascade control structures, the lack of firsthand current references imposes a tricky challenge in dealing with current constraints from the circuit and unmatched disturbances caused by load torques. To this end, a virtual reference-based fuzzy non-cascade speed control scheme is proposed for PMSM systems. First, unmatched load torques are estimated by a fuzzy disturbance observer. And then, a novel virtual reference planner is constructed to inject the estimated unmatched disturbance into the control channel as feedback compensation. This improves anti-disturbance performance, while keeping the current re-sponse within the feasible domain and reducing speed overshoot. Furthermore, the non-cascade speed regulation problem is con-verted into a system stabilization problem and addressed by the composite control strategy based on the non-parallel distributed compensation. Finally, closed-loop stability with reduced con-servatism is ensured through the fuzzy Lyapunov functions and improved slack variables. Simulations and experiments show that the proposed scheme achieves excellent speed response without overcurrent even under mismatched load torque variations.