# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 1 Introduction  
In complex and unknown extraterrestrial environments, path planning and target recognition and localization face significant challenges, including but not limited to the uncertainty of environments, sensor limitations, and the real-time requirements of algorithms. The constraints posed by communication delays with Earth and limited onboard processing capabilities make it crucial to develop solutions capable of independent navigation and scientific research using sparse maps and predetermined targets with minimal computational demands, without continuous guidance from Earth. In the current research and applications within related fields, mainstream Simultaneous Localization and Mapping (SLAM) technologies can achieve both localization and map construction simultaneously, but they carry a heavy computational burden when dealing with large-scale environments [1, 2]. A common issue with these technologies is their high dependency on environmental features and the potential for inaccurate or failed localization under specific conditions (such as extreme lighting or reflective surfaces). Furthermore, complex environments may lack distinct feature points or structures, leading to difficulties in loop closure detection. This renders traditional feature matching algorithms ineffective, thereby affecting the accuracy and stability of SLAM point cloud loop closures. Moreover, although SLAM based on deep learning has made progress in depthestimation [3], unsupervised learning methods require explicit ground truth information, which is difficult to gather in certain scenarios, and there are issues related to dataset errors introduced by sensor accuracy limitations. Overall, while SLAM technology performs well in indoor environments with ample computational power, its superiority is challenged under complex conditions with limited computational resources. Recent research on spacecraft maneuvering, such as that by [PERSON] et al., highlights the role of artificial attractive-repulsive potentials in tackling obstacles during automated rendezvous operations under positional constraints [4]. This framework offers insights into navigating uncertain environments, balancing attractive forces toward target destinations with repulsive forces from potential obstacles--a concept adaptable to planetary exploration with autonomous robotic systems. Other recent advancements in adaptive path planning have enhanced performance in complex environments. [PERSON] and [PERSON] introduced the AIT* and EIT* algorithms, which utilize asymmetric bidirectional search strategies [5]. These algorithms continuously share information between searches, optimizing both path length and obstacle avoidance efficiency, making them highly effective for real-time applications. In addition, [PERSON] et al. developed a Dynamic Parameter Adaptive Path Planning Algorithm (DPARL), which introduces a dynamic parameter adjustment strategy to improve path stability and convergence speed in environments lacking prior information [6]. This adaptability enhances the algorithm's performance in complex, unpredictable settings. These approaches emphasize adaptive adjustments, improving efficiency in challenging, dynamic scenarios. Therefore, this study aims to explore low computational consumption solutions for autonomous path planning and target search under sparse map conditions to overcome these challenges. This study introduces two key innovations in the theoretical research and design applications of path planning, target recognition and localization, and multi-task mixed control under conditions of sparse maps and dynamic environments. Firstly, it presents an improved A* path planning algorithm with an adaptive heuristic function specifically designed for low-resolution lunar surface maps. This innovation enhances the efficiency and accuracy of path planning under sparse map conditions and delves into the impact of different heuristic functions on the performance of the A* algorithm [7; 8; 9; 10; 11].