In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework explicitly incorporates path tracking constraints during path generation while simultaneously considering path characteristics in the control process. The framework comprises two principal components: (1) an enhanced Dynamic Window Approach (DWA) for the local path planning module, introducing adaptive sub-goal selection method and improved path evaluation functions; and (2) a modified Model Predictive Control (MPC) for the path tracking module, with a curvature-based reference state online changing strategy. Comprehensive simulation and real-world experiments demonstrate the framework’s operational advantages over conventional methods.

Navigation is a fundamental technology for mobile robots. However, many trajectory planning methods suffer from curvature discontinuities, leading to instability during robot operation. To address this challenge, this paper proposes a navigation scheme that adheres to the kinematic constraints of a two-wheeled differential-drive robot. An improved and efficient RRT algorithm is employed for global navigation, while an adaptive clothoid curve is utilized for local trajectory smoothing. Simulation results demonstrate that the proposed method effectively eliminates curvature discontinuities and enhances operational efficiency.

Common single-line 2D LiDAR sensors and cameras have become core components in the field of robotic perception due to their low cost, compact size, and practicality. However, during the data fusion process, the randomness and complexity of real industrial scenes pose challenges. Traditional calibration methods for LiDAR and cameras often rely on precise targets and can accumulate errors, leading to significant limitations. Additionally, the semantic fusion of LiDAR and camera data typically requires extensive projection calculations, complex clustering algorithms, or sophisticated data fusion techniques, resulting in low real-time performance when handling large volumes of data points in dynamic environments. To address these issues, this paper proposes a semantic fusion algorithm for LiDAR and camera data based on contour and inverse projection. The method has two remarkable features: (1) Combined with the ellipse extraction algorithm of the arc support line segment, a LiDAR and camera calibration algorithm based on various regular shapes of an environmental target is proposed, which improves the adaptability of the calibration algorithm to the environment. (2) This paper proposes a semantic segmentation algorithm based on the inverse projection of target contours. It is specifically designed to be versatile and applicable to both linear and arc features, significantly broadening the range of features that can be utilized in various tasks. This flexibility is a key advantage, as it allows the method to adapt to a wider variety of real-world scenarios where both types of features are commonly encountered. Compared with existing LiDAR point cloud semantic segmentation methods, this algorithm eliminates the need for complex clustering algorithms, data fusion techniques, and extensive laser point reprojection calculations. When handling a large number of laser points, the proposed method requires only one or two inverse projections of the contour to filter the range of laser points that intersect with specific targets. This approach enhances both the accuracy of point cloud searches and the speed of semantic processing. Finally, the validity of the semantic fusion algorithm is proven by field experiments.

Model-based approaches are essential for designing cyber-physical systems, which adopt the formal models to simultaneously form the specifications and enable the verification at an early stage. Aimed to model the complex structure and continuous-discrete hybrid behavior of cyber-physical systems, this paper mathematically defines a dynamic relational system so that the cyber-physical system can be regarded as dynamic relational systems in a hierarchical structure and each dynamical relational system is a triple of dynamic attributes, subsystems, and hybrid relations between attributes and subsystems. Every hybrid relation contains a tuple and a predicate to govern the system behaviors. By utilizing the dynamic relational system, a parametric abstraction is then performed to specify the design requirements and schemes. It can represent the structure and behaviors of multiple cyber-physical system design schemes in an integrated manner. With a mathematical foundation, the constructed relational models are beneficial for structural analysis and behavior verification. An implementation case of a friction-driven plate conveyor is presented to illustrate the design specification with relational models, and the connectivity analysis and behavior verifications are carried out to show the effectiveness and engineering practicability of the achieved models.

Currently, multi-sensor fusion for point cloud semantic segmentation plays a pivotal role in robotics and autonomous driving. Lidar and camera are two commonly used sensors, each offering different data modalities. However, fusion algorithms leveraging these modalities face significant challenges in achieving effective integration, the practical application of these methods has yielded unsatisfactory results. To address these issues, this paper proposes an object-based semantic fusion algorithm of Lidar and camera via inverse projection, which effectively integrates the information from both sensors and performs accurate semantic segmentation. We first propose a calibration method for Lidar and camera based on arc features in the environment, which derives the projection matrix between sensors and enhances the adaptability of the calibration process to environmental features. A multi-dimensional semantic segmentation algorithm based on inverse projection is designed, which is suitable for both 2D and 3D laser point clouds. The segmentation region is obtained by inverse projection of the bounding box, effectively reducing the influence of background points on the segmentation results and improving fusion efficiency. Additionally, distance-adaptive clustering is employed to mitigate the sensitivity of sensor systems to distance and point cloud sparsity. Building on these, we propose the object-based semantic fusion algorithm via inverse projection that exploits perceptual information from both Lidar and camera data. This approach achieves higher accuracy compared to existing Lidar-camera fusion semantic segmentation algorithms. Numerous experiments conducted on the SemanticKITTI dataset demonstrate the superiority of our approach, with a mean Intersection over Union (mIoU) outperforming the state-of-the-art method by 1.4%. Field experiments further validate the effectiveness of our proposed algorithm.