This paper presents the dynamic hierarchical attention graph neural network (DHA-GNN), an innovative framework designed for real-time task allocation in UAV swarms navigating complex, dynamic environments, such as urban cityscapes and rugged mountainous terrains. DHA-GNN integrates hierarchical feature aggregation with adaptive multi-level attention mechanisms to model evolving graph structures, capturing real-time interactions among heterogeneous UAVs, tasks, and environmental dynamics. The methodology employs self-learning graph representations to optimize task prioritization and resource allocation under constraints like dynamic obstacles, communication disruptions, and shifting mission priorities. Extensive simulations across diverse scenarios, including industrial facilities, forested regions, and counter-UAV operations, demonstrate that DHA-GNN achieves a task allocation accuracy of 98.7%, outperforming traditional methods by 15% in efficiency and reducing decision latency to 0.0023 milliseconds. It excels in urban (98.2% accuracy) and rugged terrains (97.6% accuracy), ensuring robust adaptability. These results establish DHA-GNN as a leading solution for UAV swarm intelligence, significantly enhancing operational efficiency in disaster response, surveillance, and military applications.

Task allocation in complex multiagent systems involves assigning tasks to agents with varying capabilities to optimize overall performance. The challenge lies in selecting the most suitable agent for each task, considering the agents’ heterogeneity and the intricate relationships between tasks. Traditional methods often fail to capture this complexity. To address these limitations, we propose the graph multiagent task allocation neural network (GMATANN), a novel approach utilizing a graph attention mechanism. GMATANN models the interactions between agents and tasks through a task-agent graph, where both agents and tasks are represented as nodes, and their associations are depicted as edges. The graph attention mechanism is crucial for capturing the key relationships and ensuring effective information flow between nodes. By learning attention weights, the network automatically identifies which agents are best suited for specific tasks. We employ a neural network framework based on this attention mechanism to train and evaluate the method. Simulation experiments demonstrate the effectiveness of GMATANN, achieving a task allocation accuracy of 92.3% and a reliability of 94.2%, outperforming traditional approaches. This innovative method offers a new strategy for complex task allocation in multiagent systems, providing an adaptive solution that selects suitable agents for diverse tasks, thereby enhancing system efficiency.

Efficient mission planning, including task allocation and path planning, is crucial for the successful operation of unmanned aerial vehicles (UAVs) and connected autonomous vehicles (CAVs) in complex scenarios. This article introduces an innovative mission planning approach that employs a collaborative model combining graph neural networks (GNNs) and Transformers to meet the intricate requirements of coordinating UAVs and CAVs. Our model excels in dynamic task allocation and accurate path planning, thereby boosting operational efficiency and reducing computational demands. We outline the shortcomings of current methods, notably their limited adaptability to dynamic changes and their substantial computational costs. By utilizing GNNs to capture complex interrelations and Transformers for effective information processing, our approach achieves greater adaptability and scalability. Experimental results demonstrate that our model surpasses leading methods, showing a 12% improvement in task allocation accuracy for UAVs and 10% for CAVs. Furthermore, we assess the model’s performance under various conditions, confirming its robustness and adaptability. This research provides a holistic solution for mission planning in UAV and CAV systems, setting the stage for future enhancements in autonomous vehicle coordination across logistics, surveillance, and disaster management sectors.

This paper deals with the texture mapping of a triangular mesh model given a set of calibrated images. Different from the traditional approach of applying projective texture mapping with model parameterizations, we develop an image-space texture optimization scheme that aims to reduce visible seams or misalignment at texture or depth boundaries. Our novel scheme starts with an efficient local (and parallel) texture adjustment scheme at these boundaries, followed by a global correction step to rectify potential texture distortions caused by the local movement. Our phased optimization scheme achieves 50$\sim$∼100 times speed up on GPU (or 6× on CPU) compared to previous state-of-the-art methods. Experiments on a variety of models showed that we achieve this significant speed-up without sacrificing texture quality. Our approach significantly improves resilience to modeling and calibration errors, thereby allowing fast and fully automatic creation of textured models using commodity depth sensors by untrained users.

A new type of state observer for nonlinear systems is presented in this paper. This observer is a hybrid of linear and nonlinear parts: it is based on a conventional linear observer design, and augmented by a neural network. The neural network approximates only the nonlinear part of the system. The state estimation error is proved to approach zero asymptotically.