Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control
Shankar A. Deka (Aalto University)
Sujet Phodapol (KTH Royal Institute of Technology)
Andreu Matoses Gimenez (TU Delft - Learning & Autonomous Control)
Victor Nan Fernandez-Ayala (KTH Royal Institute of Technology)
Rufus Wong (KTH Royal Institute of Technology)
Pian Yu (University of Oxford)
Xiao Tan (KTH Royal Institute of Technology)
Dimos V. Dimarogonas (KTH Royal Institute of Technology)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.