# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 5 Conclusions  
This study enhanced the traditional A* algorithm's evaluation function, augmenting its path planning capabilities in sparse and complex environments. The integration of an adaptive heuristic function allows the algorithm to maintain high levels of safety and robustness even with limited map accuracy. Additionally, this study proposed the DETIL algorithm, which includes modules for identifying unknown obstacles and spatiotemporal clustering via DBSCAN, thus reducing the computational load on intelligent agents during real-time tasks. Simulation experiments and analyses verified the effectiveness and robustness of the multi-task control scheme, which combines the adaptive heuristic A* algorithm with the DETIL algorithm. Despite these improvements, the proposed adaptive heuristic function presents certain limitations, particularly in extreme environments where a substantial portion of the map is characterized by steep gradients or rapid elevation changes. In such scenarios, the algorithm's performance may be affected due to the challenges of accurately adapting path weights to abrupt terrain shifts. Future research will focus on refining the adaptive heuristic function to improve planning speed and efficiency, with an emphasis on scaling the algorithm for larger maps and various terrains, including those with more pronounced topographical variations. These adaptations could enhance the applicability of the proposed algorithms across a broader range of real-world, challenging environments. In summary, both the improved A* algorithm and the DETIL algorithm demonstrate significant advancements in path planning, particularly in handling sparse maps, elevation changes, and enhancing obstacle identification accuracy. The adaptive A* algorithm effectively reduces expanded nodes and maintains a safe, smooth path, though its computational demands remain a consideration in resource-constrained environments. Meanwhile, the DETIL algorithm excels in clustering obstacles with high accuracy, reducing the agent's computational load. However, both algorithms may encounter limitations in scenarios with dense or highly varied clusters, extreme elevation gradients, or rapidly changing obstacles. Future work could focus on optimizing the adaptive A* heuristic to further balance computational efficiency and robustness, as well as refining DETIL's clustering method to enhance performance in these challenging conditions, ensuring reliable adaptability across diverse and complex terrains. L.W. : software, writing--original draft, methodology, editing; S.Y. : writing--review, methodology, editing; M.L. : writing--review, software, editing; X.W. : writing--review, editing. All authors have read and agreed to the published version of this manuscript. Figure 8: The blue gradient dots represent interest targets discovered sequentially during the task, the red dots are the class centers obtained by the clustering algorithm, the yellow dots are the actual positions of the interest targets, and the purple dots are the final resting positions of the intelligent agent. **Funding:** The computations in this paper were run on the cluster supported by the Center for High Performance Computing at Shanghai University of Engineering Science, and this work was supported by the Young Elite Scientists Sponsorship Program by CAST (Grant No. 2022 QNRC001), National Technology Support Program Project \"Research and Demonstration of Automatic Production Lines for Precision Assembly of Electronic Products\" (Grant No. 2015 BAF10B00), and Shanghai Municipal Science and Technology Commission Research Plan Project \"Dyeing Robot Management Software System\" (Grant No. 17511110204). **Data Availability Statement:** The code and datasets supporting the reported results in this study can be found at [[https://github.com/qiuyue97/MoonRover](https://github.com/qiuyue97/MoonRover)]([https://github.com/qiuyue97/MoonRover](https://github.com/qiuyue97/MoonRover)) (accessed on 23 March 2024). **Conflicts of Interest:** The authors declare no conflicts of interest.