In this paper, we tackle the state transformation problem in non-strict full state-constrained systems by introducing an adaptive fixed-time control method, utilizing a one-to-one asymmetric nonlinear mapping auxiliary system. Additionally, we develop a class of multi-threshold event-triggered control strategies that facilitate autonomous controller updates, substantially reducing communication resource consumption. Notably, the self-triggered strategy distinguishes itself from other strategies by obviating the need for continuous real-time monitoring of the controller’s state variables. By accurately forecasting the subsequent activation instance, this strategy significantly optimizes the efficiency of the control system. Moreover, our theoretical analysis demonstrates that the semi-global practical fixed-time stability (SPFTS) criterion guarantees both tracking accuracy and closed-loop stability under state constraints, with convergence time independent of initial conditions. Finally, simulation results reveal that the proposed method significantly decreases the frequency of control command updates while maintaining tracking accuracy.

This work develops PF-4DGS, an novel framework for 4D Gaussian splatting that addresses the challenges associated with the reliance on accurate prior knowledge of camera poses in dynamic scene modeling. Our approach employs a pose-free optimization strategy that simultaneously estimates camera parameters and reconstructs the scene within a unified framework. We introduce a stable initialization technique and an efficient joint optimization loop that simultaneously improves scene reconstruction and camera tracking. Comprehensive evaluations on real-world datasets demonstrate that PF-4DGS achieves accuracy comparable to leading methods, even without prior camera pose information. This advancement marks a huge breakthrough in Gaussian splatting and promotes the application of this technique in dynamic environments.

Exploring complex environments using heterogeneous robot swarms (RSs) is a considerable challenge in terms of coordination, sensing, and information fusion. Existing approaches suffer from a lack of systematic analysis that fully exploits the complementary capabilities of heterogeneous agents. To bridge this gap, we propose a novel spatial calibration framework that integrates both virtual and physical calibration mechanisms to enable coordinated operation between two distinct robot swarms, RS-A and RS-B. RS-A, characterized by high mobility and a broad field of view, performs continuous, large-scale monitoring and identifies candidate regions of interest. RS-B, equipped with high-precision sensors, is dispatched to these regions to conduct fine-grained data collection and return accurate environmental information, facilitating comprehensive environmental mapping. To this end, we develop a distributed control method for spatial partitioning, position optimization, and information exchange within the swarm, based on improved coverage control and a flooding-based broadcast algorithm for intra-swarm communication. We further design a control architecture that enables inter-swarm collaboration. The proposed framework effectively addresses the limitations of homogeneous RSs in environmental exploration by integrating fast, coarse-grained surveillance with slow, fine-grained investigation through heterogeneous coordination. Finally, the effectiveness of our proposed framework is validated through simulation results.

Recent advances in robotics have expanded the potential of microrobot swarms (MRSs) in medicine, yet clinical deployment remains limited due to reliance on non-autonomous systems. This study proposes an event-triggered distributed coverage control framework that enables MRSs to autonomously detect and treat membrane lesions. To model lesion dynamics accurately, we introduce a coupled reaction-diffusion equation and a Hawkes process that capture spatial spread and temporal emergence. This model informs a modified Lloyd algorithm to guide MRSs toward the centroids of Voronoi cells, optimizing drug release over pre-existing lesion areas. Furthermore, we design an event-triggered mechanism prioritizing treatment of newly emerging lesions, redirecting microrobots to lesion centers for prioritized response. This adaptive framework effectively addresses lesion proliferation and promotes membrane healing. Simulations demonstrate improved coverage efficiency and lesion containment compared to conventional strategies.

LiDAR-based object detection is a challenging task for autonomous navigation systems, especially in pedestrian-rich environments. Recently, the integration of deep learning techniques with lidar-generated point cloud data has advanced object detection and segmentation in many scenarios. However, current lidar-based methods usually struggle to accurately detect small-sized objects, such as pedestrian and cyclist, causing severe safety and reliability concerns for autonomous vehicles. This study refines structural design of lidar based neural networks to enhance precision and recall metrics for the identification of small entities. Specifically, we introduce CP-RCNN, a novel lidar object detection framework that combines state of the art voxelization and feature extraction techniques. Extensive ablation experiments demonstrate that our method has improved performance in the detection of pedestrians and cyclists. Furthermore, this paper also proposes a novel neural network structure named Centerpoint-RCNN, which not only maintains high precision in vehicle classification but also achieves an impressive inference speed of 15Hz on the NVIDIA RTX 4090 graphics processing unit.