Mixed Reality (MR) telecollaboration aims to enable users to share local objects as real-time synchronized virtual replicas to remote partners and collaborate on physical tasks as if they were co-located. However, in everyday scenarios with mobile and easy-to-setup MR devices, visualizing shared objects in a single modality, ranging from 2D images to 3D reconstruction, struggles to simultaneously optimize all the aspects of Spatiality, Fidelity, and Real-time performance. To overcome this issue, existing methods explore integrating multiple visualization modalities to leverage their respective advantages in subsets of the three aspects. However, they focus on fixed modality combinations without considering user-centered task contexts and workflow, where users may prioritize different aspects of the visualization across task phases. Moreover, they lack support for switching or require manual switching across modalities, which could become disruptive and tiring. In this paper, we propose adapting object visualization based on spatiotemporal contexts in telecollaboration. Specifically, we first couple task type with the user's relative viewing distance as the spatial context, and examine its impact on users' prioritized visualization aspects, and the corresponding switching thresholds. With differing generation speeds of modalities, we then explore temporal switching schemes when the preferred modality is not immediately available. With the obtained design choices, we implement CollabVisAdapt, a proof-of-concept prototype that supports automatic adaptation of object visualization based on spatiotemporal contexts in MR telecollaboration. A user study in remote maintenance verifies the effectiveness of the proposed workflow with adaptive visualization and the usability of the system.

In order to improve the trajectory tracking accuracy and environmental adaptability of a quadrotor unmanned aerial vehicle (UAV), an adaptive robust trajectory tracking controller for a quadrotor UAV with uncertain environment parameters based on Backstepping sliding mode method is proposed. This controller uses adaptive estimates to approximate the unknown parameters in the system model and designs an anti-interference link to counteract the negative impact of the external environment, which compensates the control input of the UAV rotor and improves the accuracy and stability of the quadrotor UAV trajectory tracking. Firstly, the mathematical model of the quadrotor UAV in the interference environment is established. Secondly, the tracking target of the quadrotor UAV is divided into attitude target and position target. Then, the adaptive method and Backstepping sliding mode method are used to design the update laws of the estimates and the rotor control input equations. Meanwhile, the appropriate Lyapunov function is established to verify the asymptotic stability of the UAV attitude system and position system. Finally, simulation experiments are carried out to compare the trajectory tracking performance of the quadrotor UAV with various controllers, and the effectiveness and superiority of the proposed controller are verified.Note to Practitioners—This paper mainly focuses on studying a trajectory tracking controller suitable for the dynamics of quadrotor UAVs, especially its application in resisting unknown environmental parameters and disturbances. In practice, adaptive predictive laws are used to estimate model parameter perturbations, and this controller is applied to the rotor control in yaw, pitch, and roll directions, enabling the UAV to track the established reference trajectory. Meanwhile, our backstepping sliding mode controller can effectively improve the robustness of the UAV, ensuring flight accuracy while reducing interference from environmental parameters. In flight control, it is worth studying to solve the external wind field interference and modeling deviation. Parameter prediction based on adaptive methods can effectively handle these unknown variables and prevent UAVs from oversteer in complex environments. The simulation results have verified that the proposed controller can enable the UAV to track the artificially set spiral ascent trajectory, demonstrating high application value in interference environments. In the future, we plan to design an intelligent control method that does not rely on physical modeling, rather than precise models. With this change, we will further improve the application realizability of quadrotor UAV in real world scenarios.

The intricate multicomponent coupling dynamics inherent in complex electromechanical systems often result in the aliasing of motor health status signals, posing a significant challenge for fault diagnosis techniques. To tackle this issue, this article presents an interpretable leakage flux pattern method, which introduces the motor mechanism knowledge system into the image representation method and enables the data-driven fault diagnosis method to have common knowledge with humans. Initially, the relationship between the magnetic field inside the motor and the leakage flux outside the motor is analyzed. Subsequently, the leakage flux pattern method is proposed to reconstruct the distribution of the magnetic induction lines inside the motor with a noninvasive measurement method. Furthermore, a lossless data compression technique grounded in causality is proposed, alongside a semantic-aware matching method for causal features. Finally, an interpretable fault diagnosis framework is proposed. The experimental outcomes demonstrate the excellent efficiency and performance of the proposed method.