This article investigates the relocalization of robots in indoor environments. To achieve this, indoor corners were classified into eight distinct categories, and a two-dimensional grid map was constructed using various sensors. Deep learning technology was employed to extract semantic information from corners, and the Bayesian method was used to build a semantic corner map incrementally. Additionally, the class attributes and positional relationships for each corner were explored, thereby facilitating the establishment of a dimension chain for semantic corners. Furthermore, a fast and efficient method was introduced for retrieving this dimension chain. During the relocalization process, the dimension chain of semantic corners was utilized for initial positioning, followed by the application of improved adaptive Monte Carlo localization (AMCL) algorithm for precise localization. Through comparative analysis using AMCL and several state-of-the-art methods, superior performance in both localization success rate and real-time implementation was demonstrated. Finally, extensive relocalization experiments were conducted to validate the effectiveness of the proposed method.

Marine-vehicle manipulators, which represent a kind of mechanical systems installed on marine surface or underwater vehicles, are mostly suffering from the problem of waves (or ocean currents)-caused base oscillations. The oscillations have a significant impact on system stability. Numerous control strategies have been investigated, but the majority of them are concentrated on the control’s robust performance. This study focuses on an innovative marine-vehicle manipulator (ammunition transfer manipulator on warships) with novel compliant actuators (series elastic actuators), for which the control performance of convergence speed and flexible-vibration suppression should also be considered. To address these issues, this paper proposes a unique hybrid control based on the singular perturbation method, by which the control problem is decomposed into two time scales. In the slow time-scale, it is given a rapid trajectory tracking controller that integrates the computed torque method and the terminal sliding mode control law with a novel reaching law (variable power log reaching law). For the fast time-scale control, a derivative-type controller is used to achieve the suppression of the flexible vibrations. To demonstrate the effectiveness of the proposed control method, theoretical proofs and numerical simulations are both presented. According to our knowledge, this study presents the first control strategy for rapid robust control of marine-vehicle manipulators that are subject to base-oscillation-caused disturbance and compliant-actuator-induced flexible vibrations.