Robotic Wire Arc Additive Manufacturing (WAAM) for the fabrication of metallic parts is garnering interest due to its advantages of low capital investment, high deposition rates, and good material properties. Although many achievements have been made, the build direction of WAAM remains confined to the vertical-up direction, requiring additional supporting structure to be deposited while fabricating metallic parts with overhanging features. In this work, a novel multi-directional WAAM process, using robotic Gas Metal Arc Welding (GMAW), to additively manufacture metal components in multiple directions is presented. Several novel modules, including positional bead modeling, multi-direction slicing, and deposition process optimization of the deposition process are highlights of this work. In addition, the performance of the proposed multi-directional WAAM strategy is evaluated by the successful deposition of a sample workpiece with complex geometrical features. The proposed multi-directional WAAM process would significantly reduce the manufacturing time and cost.

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

Multi-UAV cooperative encirclement tasks have attracted considerable attention in areas such as military defense and target interception. Fixed-wing UAVs face substantial challenges due to intrinsic dynamic limits, including their minimum velocity and turning radius, particularly when engaging evasive target and navigating in obstacle environments. This paper presents a hybrid deep reinforcement learning approach, in which a cooperative task environment is developed for fixed-wing UAVs that jointly integrates encirclement and obstacle avoidance. A composite MADDPG framework enhanced with curriculum learning is designed, employing progressive task staging and reward optimization to accelerate convergence and improve policy stability. Simulation results demonstrate that the proposed method achieves single-step encirclement success rates exceeding 80% in complex environments, while maintaining 10-step success rates around 70%, thereby strengthening both encirclement capability and obstacle avoidance safety in fixed-wing UAV swarm. This study provides new insights into the intelligent cooperative control of fixed-wing UAVs in high-risk missions.

Maritime security is vital to national security. The application of unmanned aerial vehicle (UAV) clusters in marine anti-submarine warfare (ASW) presents a new and significant challenge worthy of in-depth study. Based on the anti-submarine principle of geomagnetic anomaly detection and the Find-Fix-Track-Target-Engage-Assess (F2T2EA) framework, this paper divides UAV cluster ASW operations into two stages: regional coverage and cooperative convergence. The regional coverage stage enables the UAV cluster to perform a broad search for submarines, while the cooperative convergence stage facilitates the precise positioning of detected submarines. In simulation, the combat scenario of five UAV clusters against a submarine is carried out. In the given area, the UAV can locate and stalk the target submarine in limited time. Simulation results demonstrate the feasibility of the proposed approach, providing reference for advancing marine ASW capabilities and related research.

Wire Arc Additive Manufacturing (WAAM) is well suited for the manufacture of sizeable metallic workpieces featuring medium-to-high geometrical complexity due to its high deposition rate, low processing conditions limit, and environmental friendliness. To enhance the current capability of the WAAM process for fabricating structures with complex geometry, this paper proposes a robot-based WAAM strategy adapted specifically for fabricating free-form parts with wire structures composed of multiple struts. Contributions in this work include: (i) The study of bead modelling, which establishes optimal welding parameter selection for the process; (ii) The novel manufacturing strategy, including the adaptive slicing methodology and height control system for accurately depositing every single strut; (iii) Detailed manufacturing procedures for multi-strut branch intersections as well as the collision-free path planning to control the overall fabrication process. To verify the effectiveness of this proposed WAAM approach, two complex wire structures were fabricated successfully, indicating the feasibility of the proposed fabrication strategy.