Self and Target Locating with Cooperation of Heterogeneous Unmanned Vehicles in the Denial Environment

The growing reliance on unmanned vehicles, such as drones and autonomous vehicles, has revolutionized both military and civilian applications, particularly in challenging environments where traditional reconnaissance methods fail. These systems are essential for tasks such as self-localization and target localization, particularly in denial environments where GPS and communication networks are compromised. However, effective cooperation of heterogeneous unmanned systems under these conditions remains a significant challenge. Without external positioning systems, ensuring reliable navigation and mission execution requires innovative approaches to localization, mapping, and path planning. This paper proposes an integrated system that enhances the cooperation between ground robots and aerial drones for self-localization and target localization in GPS-denied environments. The system incorporates advanced techniques such as the Extended Kalman Filter (EKF) for localization, G-mapping for environment mapping, Dijkstra's algorithm for global path planning, and the Dynamic Window Approach (DWA) for real-time local path replanning. Through simulations involving Husky robots and Hector quadcopters, the system demonstrates its ability to maintain accurate navigation and obstacle avoidance in communication-limited environments. The results demonstrate that the proposed system can successfully enable autonomous vehicles to cooperate and perform tasks reliably under challenging conditions. Future work will focus on expanding the framework to support a wider range of unmanned vehicles, improving control algorithms, and testing the system in even more complex denial environments to ensure continued robustness and adaptability.