Collaborative loco-manipulation by multiple quadruped manipulators enables handling bulky, heavy objects beyond the capabilities of individual robots. However, coordinating robot teams while navigating complex terrains and avoiding obstacles remains challenging. We propose a hierarchical control framework consisting of a model predictive control (MPC)-based manipulation planner with integrated obstacle avoidance, a geometry-aware mapping converting object trajectories into robot commands, and decentralized loco-manipulation MPC controllers. The framework supports collision-free collaborative manipulation tasks and enhances payload capacities. Validation through simulation and real-world hardware experiments with diverse quadruped robot teams demonstrates the approach’s effectiveness, robustness, and practical applicability.
Robotic unscrewing plays a pivotal role in the automation of remanufacturing processes. Despite advancements in deep learning-based screw detection, which nowadays achieve detection accuracies exceeding 98%, consistent unscrewing success remains challenging due to physical localization errors. Within prior research various vision-based methods have been developed to correct positional deviations that arise from neural network predictions. On the other hand, force-based methods have been developed to compensate for all accumulating errors during physical contact. The vision-based approaches often lack reliability, and force-based methods, although highly effective, have to date, only been validated on larger, self-centering fasteners and will result in additional wear to the unscrewing end-effector. Therefore, this study presents a comparative analysis of two robotic unscrewing pipelines designed to enhance detection accuracy, localization precision, and unscrewing time efficiency. The first baseline approach employs a single-stage, coarse-only sense-plan-act approach. The second, advanced two-stage approach integrates coarse localization with a refinement step using stereo detection, matching and triangulation. Experimental results highlight the advantages of the advanced two-stage approach, which incorporates a region-of-interest recommendation step and an eyes-in-hand stereo module for position refinement. With the pilot system developed in the presented research, vision-based precision and recall rates of 99.2% and 99.4% are obtained, with an unscrewing success rate of 99%. At the system level, these results correspond to unscrewing precision and recall of 98.2% and 98.4% with only 0.75 seconds needed to reclassify and compute the updated screw location.
To address the challenges of hyperparameter tuning and reliance on manual experience in dual-channel pulse-coupled neural networks (DCPCNN) for image fusion tasks, the paper proposes a data-driven Bayesian maximum entropy (DBME) multi-objective optimization method to enhance image fusion performance. Firstly, the source images are transformed into the NSST domain with low-frequency bands and high-frequency bands. Secondly, we design a DCPCNN model based on DBME optimization to fuse the high-frequency subbands. For the low-frequency subbands, a method based on weighted local energy and multi-scale morphological gradient fusion rule is proposed. Finally, the fused image is reconstructed by the NSST inverse transform. The results demonstrate that, compared to other fusion methods, this approach outperforms in the fusion of infrared and visible light images, as well as multi-focus images, with significant advantages in metrics such as AG, SD, SCD, and VIFF.
The transition from Industry 4.0 to Industry 5.0 has highlighted the critical role of Modular Integrated Construction (MiC), particularly in rapidly deployable modular hospitals that address urgent healthcare demands. As the final stage before delivery, fit-out directly impacts both project speed and healthcare quality. However, scheduling in this phase faces challenges from dynamic labor allocation and worker fatigue, which traditional methods struggle to handle in out-of-distribution (OOD) settings. To tackle this, we reformulate the problem as a Flexible Job-shop Scheduling Problem with Workload Constraints (WL-FJSP) and propose a memory-augmented framework that models worker-task dynamics. By incorporating adaptive gating mechanisms, the model captures fatigue variations and jointly optimizes medical task fulfillment and fit-out efficiency. Experiments show improved performance over traditional and state-of-the-art methods, with strong generalization across varying instance scales.
With Industry 4.0 advancing, manufacturing requires real-time data acquisition, device connectivity, and cloud-based analytics. However, the diversity of equipment and legacy systems within factories often hinders a seamless transition to intelligent production. To address this, this paper proposes a three-layer cloud factory architecture featuring a platform called OPC UA Fusion. Multiple OPC UA servers share data in real-time, enabling machine-to-machine scheduling and linking to a NoSQL database and the remote OPC UA Fusion Cloud. Two case studies validate the feasibility of the platform. With the help of cloud analytics, the results show improved interoperability and visualisation. Future work includes better OPC Classic support and advanced scheduling to further enhance fully automated intelligent manufacturing capabilities.
