Energy-Aware Planning for Legged Robot Performing Logistics Tasks in Agriculture Applications

Abstract Legged robots can significantly increase human productivity by performing delivery tasks, especially in unstructured agriculture fields. In large outdoor environments, legged robots typically operate independently from tethered power sources, relying on onboard batteries. If a robot runs out of energy while executing a task, it will require human intervention, resulting in delays. On the other hand, frequent battery recharging or replacement could prolong task completion times. This paper presents a systematic framework to enhance productivity for logistic tasks. The framework features a map construction utility, an energy consumption model to measure battery usage, and an energy-aware hierarchical planning approach that accounts for energy consumption and integrates appropriate battery replacement strategies to ensure that tasks are completed efficiently. Our algorithm first generates different scenarios, considering battery replacement options, payload partitioning, and speed reduction strategies. Subsequently, it employs graph search methods to identify the optimal plan that minimizes delivery completion time. We illustrate the effectiveness of our planning approach on a terrain with varying slopes and delivery tasks with different requirements. We also demonstrated that our robot can successfully traverse in narrow furrows in broccoli and cabbage farms.