Resource and Response Aware Path Planning for Long-term Autonomy of Ground Robots in Agriculture

Abstract

Achieving long-term autonomy for mobile robots operating in real-world, unstructured environments, such as farms, remains a significant challenge. Such tasks are made increasingly complex when undertaken in the presence of moving humans or livestock. These dynamic environments require a robot to be able to adapt its immediate plans, accounting for the state of nearby agents and possible responses they may have to the robot’s actions. Additionally, in order to achieve longer-term goals, consideration of the limited on-board resources available to the robot is required, especially for extended missions, such as weeding agricultural fields. To achieve efficient long-term autonomy, it is thus crucial to understand the impact that dynamic updates to an energy-efficient plan might have on resource usage whilst navigating through crowds or herds. To address these challenges, we present a hierarchical planning framework that integrates an online, dynamic path-planner with a longer-term, offline, objective-based planner. This framework acts to achieve long-term autonomy through awareness of both dynamic responses of agents to a robot’s motion and the limited resources available. This paper details the hierarchical approach and its integration on a robotic platform, including a comprehensive description of the planning framework and associated perception modules. The approach is evaluated in real-world trials on farms, requiring both consideration of limited battery capacity and the presence of nearby moving agents. These trials additionally demonstrate the ability of the framework to adapt resource use through variation of the dynamic planner, allowing adaptive behaviour in changing environments. A summary video is available at https://youtu.be/DGVTrYwJ304.