Hai Wang

Accurate localization is a critical requirement for the successful deployment of mobile robots in indoor environments, particularly those characterized by symmetrical structures and features. Symmetrical indoor environments pose unique localization challenges due to the presence of repeated patterns and structures that can confound traditional localization methods. In such environments, accurately estimating the robot's pose relative to a global reference frame becomes increasingly challenging, leading to potential errors and inefficiencies in robot navigation and task execution. This paper presents an improved deep learning-based Monte Carlo localization (DMCL) framework for global localization of a mobile robot in symmetrical indoor environment using only 2D lidar. We first, converted 2D laser data to single channel 2D projected image and an occupancy grid. This 2D projected image is used to train the neural network to regress the 3DOF of robot. Finally, we integrated this trained neural network which estimate the robot pose in environment with MCL in the weight updating stage. The performance of both Monte Carlo Localization (MCL) and in DMCL methods in symmetrical indoor environment is investigated through extensive simulation studies. Verifying the effectiveness of the proposed method our network is able to obtain position accuracy of 0.15m and scene classification accuracy to 99% in simulation.

Stereo image Super-Resolution (StereoSR) aims to reconstruct a high-resolution image from the low-resolution stereo image pairs by taking full advantage of the complementary information between the left and right views of stereo images. Although existing StereoSR methods have achieved good performances, they have not utilized the information from intra-view and cross-view fully yet and face the huge network’s parameters. In order to address the above problems, a disparate lightweight attention-based fusion network, called Dual-dimensional Interactive Parallax Attention network (DIPAnet), is proposed in this paper. Our proposed network designs an effective Dual-dimensional Interactive Parallax Attention Module (DIPAM) that employs a Spatial Channel Fusion Module (SCFM) to obtain the complementary information from the aspect of spatial dimension and the channel dimension. In the meanwhile, some lightweight Omni-Scale Aggregation Groups (OSAGs) are applied to constitute the backbone of the main network for extracting the intra-view features. Extensive comparison experiments and ablation study illustrate that our proposed DIPAnet can achieve competitive results and outperforms some state-of-the-art StereoSR methods.

This paper investigates the speed control of Permanent Magnet Synchronous Motor (PMSM). Firstly, addressing the issue that traditional sliding mode control (CSMC) can only achieve asymptotic convergence in an infinite domain, and the coupling problem between the convergence time and control parameters depth in fixed-time sliding mode control (FSMC), a type of integral pre-defined time convergence sliding mode controller (IPSMC) is proposed for PMSM speed regulation, with disturbance compensation using a Luenberger Disturbance Observer (LDOB). Secondly, based on Lyapunov stability theory, the stability of the system is proved, and the effectiveness of the proposed scheme is established. Finally, simulation results demonstrate that the proposed method can effectively enhance the dynamic performance and robustness of the PMSM speed control system.

Permanent magnet synchronous motor (PMSM) is widely used in many fields due to its high-torque and high-power density. In this paper, a fixed-time integral sliding mode (FTISM) control is proposed, which enhances the position tracking ability and anti-interference ability of PMSM, thereby improving the control performance of the system. Firstly, the integrated terminal sliding mode controller is designed, which can ensure the system has excellent control precision and rapid convergence speed. The finite-time stability of the proposed FTISM scheme is demonstrated by using Lyapunov theory. Then, a PMSM algorithm test platform based on STM32G4 is designed, which can effectively run various control algorithms. Finally, the algorithm is simulated and verified in Simulink. The results indicate that the controller has high tracking performance and strong anti-interference ability, which can significantly reduce the position tracking error of the system and weaken the chattering phenomenon of the sliding mode control (SMC).

Multi-robot task allocation and coalition formation are critical challenges in robotics, essential for applications such as disaster response, search and rescue, environmental monitoring, exploration and mapping, surveillance and security, logistics, agriculture, military operations and healthcare. Therefore, it is essential to address these challenges and develop optimal solutions for implementing these concepts in real-world scenarios to effectively execute the previously mentioned applications. Hence, this paper presents a comprehensive survey and comparative analysis of different approaches for allocating tasks to multiple robots and forming coalitions to accomplish these tasks efficiently. The paper first provides a systematic categorization of the existing methods into four different groups namely behavior-based, market-based, optimization-based, and learning-based methods. Next, it analyzes the trade-off between different objectives, including minimizing task completion time, maximizing resource utilization, and balancing workload among robots. The paper also explores the impact of robot heterogeneity, task dependencies, and communication constraints on the performance of various algorithms. Furthermore, it discusses the challenges of dynamic task allocation and coalition formation in response to changes in the environment or robot failures.
Accordingly, the paper presents a comprehensive comparative study of the surveyed approaches, highlighting their substantial features including limitations and suitability for different application scenarios. As such, the paper identifies promising research directions, including the integration of machine learning techniques and the development of hybrid algorithms. Through this systematic analysis, the main aim is to provide researchers with a comprehensive understanding of the state-of-the-art in multi-robot task allocation and coalition formation, enabling them to select the most appropriate approach for their specific requirements.

Magnetic resonance imaging (MRI) is a significant non-invasive clinical tool that can provide multi-contrast images of the same anatomical structures. Multi-contrast super-resolution (SR) utilizes the complementary information of MRI from different contrasts to achieve higher quality images. However, diffusion models used for image generation and reconstruction usually require large computational resources due to extensive iterative computations, which hinders adaptability in resource-constrained environments. To solve this problem, we propose a Multi-Contrast Enhanced Lightweight Diffusion Model (MCLDM) for MRI. The MCLDM model includes the developed Multi-Contrast Dual Attention (MCDA) module and the designed cross-channel attention mechanism modules. Specifically, the MCDA module integrates multi-contrast images into the diffusion model and applies a dual attention mechanism to these features, effectively compensating for the limitations of single-modality information and enhancing the capability of focusing on complementary information. Additionally, the designed cross-channel attention module improves feature extraction capability and computational efficiency. Experimental results show that MCLDM achieves high-quality reconstruction and balances performance and computational complexity, reducing image generation time compared to other diffusion methods.

This article proposes an improved adaptive sliding mode control (IASMC) based on a predefined-time disturbance observer (PTDO) to ensure the anti-disturbance capability and the accuracy of speed control for a permanent magnet synchronous motor (PMSM). The current control design is based on an improved sliding mode approach law, which can adjust the control current according to the change of PMSM state and mitigates system chattering. At the same time, the PTDO is introduced as a compensator to counteract interference and reduce control gain. The characteristic of the pre-defined time sliding mode enables the interference observer to complete the disturbance estimation in the pre-specified time. The closed-loop stability analysis is rigorously given by Lyapunov stability theory. The numerical simulation results confirm the validity of the proposed method.

This paper presents the development of an efficient and smooth path planning algorithm tailored for autonomous systems operating in static environments. A modified bi-directional and goal-oriented rapidly-exploring random tree (RRT) algorithm is proposed, generating an initial rough global path quickly. To further enhance the quality of this path, we introduce a two-step optimization process, involving down-sampling to reduce redundant waypoints and up-sampling to improve path resolution. A cubic curve smoothing technique is then applied to ensure the path maintains continuity and remains collision-free, even in dense, clustered environments. The algorithm is validated by simulations in environments that approximate real-world conditions using MATLAB. This work primarily focuses on improving computational efficiency and path smoothness, addressing key challenges in robotic path planning.

In this chapter, the basic ideas of real-time control systems with applications in mechatronics will be discussed. The chapter starts with the introduction of a real-time system (RTS), real-time operating system (RTOS), and digital control systems. Then several interesting engineering applications of RTS are demonstrated. The detailed arrangements of this chapter are as follows: In Sect. 1, the definition and characteristics of the RTS will first be discussed, then various controller designs with respect to RTS will be reviewed. In Sect. 2, 3, and 4, the applications and analyses for three different mechatronics systems, i.e., automotive steer-by-wire systems, electronic throttle systems, and linear motor systems, are given with detailed mathematical modeling and corresponding control design. In Sect. 5, conclusions of this chapter are drawn.
It is assumed that the reader has a solid background in basic control science and engineering or has some practical experience in the design or implementation of embedded systems.