Heterogeneous Aerial Ground Robot Collaboration for Vineyard Monitoring with Anisotropic Sensors and Inspection Waypoints
DOI:
https://doi.org/10.71465/jgs56Keywords:
Precision Viticulture, Heterogeneous Robotics, Anisotropic Sensors, Collaborative Path PlanningAbstract
The integration of autonomous robotic systems into precision viticulture represents a transformative shift in how vineyard health, yield estimation, and disease detection are managed. Traditional monitoring techniques rely heavily on manual labor or homogeneous robotic deployments, both of which suffer from significant limitations in scalability, energy efficiency, and data resolution. This paper presents a comprehensive framework for heterogeneous field-robot collaboration, specifically focusing on the synergistic deployment of Unmanned Aerial Vehicles and Unmanned Ground Vehicles equipped with anisotropic sensors. Unlike omnidirectional sensors, anisotropic sensors such as RGB-D cameras, directional multispectral imagers, and phased-array radars possess strict directional constraints, limited fields of view, and range-dependent resolution. This spatial dependency introduces profound challenges in coverage path planning and task allocation. We propose a decentralized collaborative architecture wherein aerial units conduct high-level topological mapping and anomaly detection, dynamically generating high-resolution inspection waypoints for ground units. The ground units subsequently navigate the complex under-canopy environment using a novel anisotropic coverage algorithm that maximizes sensor utilization while minimizing traversal energy. Through extensive simulated and real-world vineyard trials, the proposed heterogeneous framework demonstrates a substantial improvement in inspection efficiency, reducing overall energy consumption while maximizing canopy coverage. The results indicate that accounting for sensor anisotropy within a heterogeneous collaborative framework significantly bridges the gap between aerial macro-monitoring and ground-level micro-inspection.
References
1. Wang, W., Dai, H., Dong, C., Xiao, F., Zheng, J., Cheng, X., ... & Fu, X. (2020). Deployment of unmanned aerial vehicles for anisotropic monitoring tasks. IEEE Transactions on Mobile Computing, 21(2), 495-513.
2. Yu, D., Mahamadu, A. M., Chen, W., & Li, N. (2026). Understanding the challenges and future trends of immersive technology use in indoor pedestrian and evacuation dynamics: A systematic review. Journal of Building Engineering, 115703.
3. Sui, M., Jiang, L., Lyu, T., Wang, H., Zhou, L., Chen, P., & Alhosain, A. (2024). Application of deep learning models based on efficientdet and openpose in user-oriented motion rehabilitation robot control. Journal of Intelligence Technology and Innovation, 2(3), 47-77.
4. Qu, W., Shao, Y., Meng, L., Huang, X., & Xiao, L. (2024). A conditional denoising diffusion probabilistic model for point cloud upsampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20786-20795).
5. Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In Proceedings of CVPR.
6. Lv, Qi, et al. "Spatial-temporal graph diffusion policy with kinematic modeling for bimanual robotic manipulation." Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.
7. Qu, W., Wang, J., Gong, Y., Huang, X., & Xiao, L. (2025). An end-to-end robust point cloud semantic segmentation network with single-step conditional diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 27325-27335).
8. Xiao, Q., & Ghaffari, M. (2026). LaWM: Least Action World Models for Long-Horizon Physical Consistency from Visual Observations. arXiv preprint arXiv:2605.08279.
9. Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In Proceedings of ECCV.
10. Li, B., Zhang, R., Liang, H., Zhang, J., Zhang, J., Chen, X., ... & Wang, J. (2026). Interagent: Physics-based multi-agent command execution via diffusion on interaction graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 15253-15265).
11. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
12. Wan, H., Zhang, Y., Wang, J., Wu, D., Li, M., Chen, X., ... & Ji, X. (2025). Toward universal embodied planning in scalable heterogeneous field robots collaboration and control. Journal of Field Robotics, 42(5), 2318-2336.
13. Wan, H., Cheng, J., Deng, Y., Wu, D., Chen, Y., Lin, Z., ... & Ji, X. Towards Physics Aware Embodied Control with Graph based Object-centric Learning. ACM Transactions on Cyber-Physical Systems.
14. Peng, Q., Bai, C., Zhang, G., Xu, B., Liu, X., Zheng, X., ... & Lu, C. (2025, October). NavigScene: Bridging local perception and global navigation for beyond-visual-range autonomous driving. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 4193-4202).
15. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of MICCAI.
16. Zhang, Luyan. "MCP: A Control-Theoretic Orchestration Framework for Synergistic Efficiency and Interpretability in Multimodal Large Language Models." arXiv preprint arXiv:2509.16597 (2025).
17. Levine, S., & Koltun, V. (2013). Guided policy search. In Proceedings of ICML.
18. Li, Q., Luo, H., Cheng, H., Deng, Y., Sun, W., Li, W., & Liu, Z. (2023). Incipient fault detection in power distribution system: A time-frequency embedded deep-learning-based approach. IEEE Transactions on Instrumentation and Measurement, 72, 1–14.
19. Cheng, H., Luo, H., Liu, Z., Sun, W., Li, W., & Li, Q. (2025). Reinforcement learning based robust volt/var control in active distribution networks with imprecisely known delay. IEEE Transactions on Computational Social Systems, 1–12.
20. Kerbl, B., Kopanas, G., Leimkühler, T., & Drettakis, G. (2023). 3D Gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4), Article 139.
21. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., et al. (2016). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
22. Zhu, W., Zhang, Y., Jin, X., Zeng, W., & Zhang, L. (2025, October). Knowledge Regularized Negative Feature Tuning of Vision-Language Models for Out-of-Distribution Detection. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 3565-3574).
23. Kamal, T., Wenhong, T., Guo, J., Shafiq, M., Shuaihong, J., & Xue, R. (2026). Adaptive Probabilistic Transformer for Medical Image Segmentation. Expert Systems with Applications, 131948.
24. Cheng, Xiaoyuan, et al. "How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?." arXiv preprint arXiv:2602.02924 (2026).
25. Kavraki, L. E., Švestka, P., Latombe, J.-C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.
26. Luo, H., Feng, Q., Sun, W., Li, W., Li, Q., Zhao, W., & Liu, Z. (2026). FD-Mamba with neural observer and frequency-enhanced update for incipient feeder fault detection. IEEE Internet of Things Journal, 13(10), 20761–20772.
27. Yang, Y., Hu, H., Li, W., Li, S., Yang, J., Zhao, Q., & Zhang, C. (2023, June). Flow to control: Offline reinforcement learning with lossless primitive discovery. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 9, pp. 10843-10851).
28. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
29. Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
30. Yang, Y., Wang, Q., Li, C., Hu, H., Wu, C., Jiang, Y., ... & Xu, B. (2025, May). Fewer may be better: Enhancing offline reinforcement learning with reduced dataset. In International Conference on Learning Representations (Vol. 2025, pp. 7076-7098).
31. Wang, Siheng, et al. "C3-owd: A curriculum cross-modal contrastive learning framework for open-world detection." arXiv preprint arXiv:2509.23316 (2025).
32. Tang, A., Hiraoka, T., Hiraoka, N., Shi, F., Kawaharazuka, K., Kojima, K., ... & Inaba, M. (2024, May). Humanmimic: Learning natural locomotion and transitions for humanoid robot via wasserstein adversarial imitation. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 13107-13114). IEEE.
33. Wang, H., Li, Z., Zhang, Y., He, Z., Jiang, L., Li, K., ... & Gu, D. (2026). ConsistNav: Closing the Action Consistency Gap in Zero-Shot Object Navigation with Semantic Executive Control. arXiv preprint arXiv:2605.09869.
34. Hu, Y., Xi, A., Xiao, Q., Isaacson, S., Liu, H. X., Vasudevan, R., & Ghaffari, M. (2026). LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation. arXiv preprint arXiv:2602.12351.
35. Zhang, Y., He, Y., Shao, Y., Yao, Z., Xu, H., Dong, J., ... & Dong, Z. (2026, May). Chromouvqa: Benchmarking vision-language models under chromatic camouflaged images. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12777-12781). IEEE.
36. Brohan, A., Chebotar, Y., Finn, C., Hausman, K., Herzog, A., Ho, D., et al. (2023). RT-2: Vision-language-action models transfer web knowledge to robotic control. In Proceedings of the Conference on Robot Learning.
37. Wang, K., Hou, W., Hong, L., & Guo, J. (2025). Smart transparency: A user-centered approach to improving human–machine interaction in high-risk supervisory control tasks. Electronics, 14(3), 420.
38. Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.
39. Wang, C., Ke, L., Liu, X., Huang, D., Yao, J., & Chu, W. Explainable AI Analysis of Brake Control in CARLA Through Reference and Distilled Policies. Frontiers in Robotics and AI, 13, 1847808.
40. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
41. Wan, H., Chen, Y., Deng, Y., Wei, Z., Li, D., Lin, Z., ... & Ji, X. (2025, October). EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 12140-12146). IEEE.
42. Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. In Proceedings of ICCV.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Eliza Murray, Stella Palmer, Joonseo Jang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in the Journal of Geospace Science (JOGSS) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, copy, distribute, and adapt the work, provided proper attribution is given to the original author(s) and source.