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.

The accurate estimation of mechanical parameters is essential for the control optimization and condition monitoring of permanent-magnet synchronous motor (PMSM) drives. In this article, an online parallel estimation scheme is proposed based on a network of three interconnected extended sliding-mode observers. With a two-step mechanism derived from the developed network, this scheme can simultaneously achieve the accurate real-time observation of three mechanical parameters, including the viscous friction coefficient, the moment of inertia, and the load torque. The first step utilizes the presented network to precisely acquire the Coulomb friction coefficient by offering only one speed. With aiding from this estimated coefficient, the second step attains the high-precision online identification of the three concerned parameters by compensating the Coulomb friction torque to counteract its adverse effect. These two steps are implemented utilizing the identical network, thus, furthest avoiding increasing the estimation complexity. Besides, any additional knowledge other than the rotor speed and electromagnetic torque is no longer required for guaranteeing the asymptotic convergence of the estimation errors. Sufficient evaluations from simulations and experiments confirm the validity of the proposed scheme.

This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

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.

Acquiring the accurate knowledge of nonlinear friction and load torque is of great interest for optimizing the control behavior of permanent-magnet synchronous motor drives. In this work, a friction-and-load adaptive identification scheme based on a parallel-observer-based network with model compensation (POBN-MC) is presented. The developed network possesses a parallel structure consisting of the designed two novel observers, which involve a gain-adaptation super-twisting load torque observer and a variable-learning-rate Adaline inertia observer. A nonempirical friction model is proposed to capture friction, forming the model compensation part that is exploited for correcting the torque input of the network. With a two-step mechanism derived from the POBN-MC, the proposed scheme attains the online adaptive identification of the friction and load torque in a manner that integrates both accuracy and simplicity. In the first step, an explicit mapping relationship between the nonlinear friction torque and the rotor speed is determined with the speed response triggered by the natural deceleration. The second step accomplishes the online observation with regard to friction-and-load information matching the real-time operating conditions. Sufficient theoretical analyses, as well as the validations of numerous simulations and experiments, are presented to support the suggested scheme.