Toward Collaborative Multitarget Search and Navigation with Attention‐Enhanced Local Observation

Collaborative multitarget search and navigation (CMTSN) is highly demanded in complex missions such as rescue and warehouse management. Traditional centralized and decentralized approaches fall short in terms of scalability and adaptability to real‐world complexities such as unknown targets and large‐scale missions. This article addresses this challenging CMTSN problem in three‐dimensional spaces, specifically for agents with local visual observation operating in obstacle‐rich environments. To overcome these challenges, this work presents the POsthumous Mix‐credit assignment with Attention (POMA) framework. POMA integrates adaptive curriculum learning and mixed individual‐group credit assignments to efficiently balance individual and group contributions in a sparse reward environment. It also leverages an attention mechanism to manage variable local observations, enhancing the framework's scalability. Extensive simulations demonstrate that POMA outperforms a variety of baseline methods. Furthermore, the trained model is deployed over a physical visual drone swarm, demonstrating the effectiveness and generalization of our approach in real‐world autonomous flight. Addressing the challenges of collaborative multitarget search and navigation in complex 3D environments, this article presents the POsthumous Mix‐credit assignment with Attention (POMA) framework. POMA innovatively combines adaptive learning and mixed credit assignment, integrating an attention mechanism for scalable local observation management. Demonstrated through simulations and real‐world drone swarm deployment, POMA significantly outperforms established baseline methods.