Unmanned aerial vehicles (UAVs) offer several advantages, including high mobility, flexible deployment, low cost, and strong adaptability to complex environments, making them highly promising for applications such as disaster search and rescue, environmental monitoring, inspection, and reconnaissance. For target exploration tasks in unknown environments, multiUAV systems can expand the search area, improve exploration efficiency, and enhance the robustness of task execution through cooperation, which makes this problem of significant research interest. However, such tasks still face several challenges, including partial observability of environmental information, complex cooperative decision-making, and difficulties in credit assignment among multiple UAVs. Reinforcement learning is capable of learning decision-making policies autonomously through interaction with the environment, providing a new perspective for solving cooperative exploration problems in complex environments. To address these issues, we propose a cooperative decision-making method for multi-UAV target exploration. By incorporating target-related information, the proposed method enhances the cooperative exploration capability of UAVs in unknown environments, while a tailored reward design is adopted to improve the coordination efficiency of multiple UAVs. Experimental results show that the proposed method exhibits strong adaptability to different team sizes and sensor configurations, learns effective cooperative behaviors, and outperforms classical exploration methods across multiple performance metrics, thereby demonstrating its effectiveness in multi-UAV target exploration tasks.
Batuo Zhang, Lei Liu, Zhongmin Yan et al.· Fall Joint Computer Conferen...· 0 citations
Peptides play important roles in biological processes and biomedical applications, and their hemolytic (Hemo) and nonfouling (NF) properties directly affect their safety and translational potential. Therefore, accurate predictive models are essential for the rational design of functional peptides. Although existing multimodal peptide property prediction methods can jointly exploit sequence and structural information, their structural encoders still rely primarily on local graph convolution and their contrastive objectives are largely focused on cross-modal alignment. Consequently, they remain limited in modeling long-range structural dependencies and in enhancing intramodal discriminability. To address these limitations, we propose a multimodal dual-contrastive learning framework for peptide property prediction, which improves both the structural encoder and the contrastive learning strategy to enhance the quality of joint sequence-structure representations. Specifically, ProtBERT is adopted as the sequence encoder, and a hierarchical GNN-Transformer structural encoder is constructed to capture local topological patterns and long-range structural dependencies. In addition, a parallel graph spatial channel attention module is introduced to enhance task-relevant structural features. Within a shared embedding space, we further design an interintra hybrid supervised contrastive learning strategy to jointly optimize sequence-structure alignment and intramodal class discriminability. Experimental results show that the proposed method achieves overall performance superior to baseline models on both hemolysis and NF prediction tasks, providing an effective framework for multimodal representation learning in peptide-property prediction.
Jiajie Cai, Shuwen Xiong, Yuntao Yang et al.· ACS Synthetic Biology· 0 citations
