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

TL;DR

This paper presents a hierarchical planning framework for ground robots in agriculture that adaptively manages resources and responds to moving humans or livestock, enabling long-term autonomous operation in unstructured environments.

Contribution

It introduces a novel hierarchical planning approach combining online local and offline long-term planning for resource-aware, adaptive robot navigation in dynamic agricultural settings.

Findings

Successful real-world farm trials demonstrating adaptive resource management.

Framework effectively responds to moving humans and livestock.

Enhanced long-term autonomy in unstructured environments.

Abstract

Achieving long-term autonomy for mobile robots operating in real-world unstructured environments such as farms remains a significant challenge. This is made increasingly complex in the presence of moving humans or livestock. These environments require a robot to be adaptive in its immediate plans, accounting for the state of nearby individuals and the response that they might 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 an agricultural field. To achieve efficient long-term autonomy, it is thus crucial to understand the impact that online dynamic updates to an energy efficient offline plan might have on resource usage whilst navigating through crowds or herds. To address these challenges, a hierarchical planning framework is proposed, integrating an online local dynamic path planner with an offline longer-term objective-based planner. This framework acts to achieve long-term autonomy through awareness of both dynamic responses of individuals 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 individuals. These trials additionally demonstrate the ability of the framework to adapt resource use through variation of the local dynamic planner, allowing adaptive behaviour in changing environments. A summary video is available at https://youtu.be/DGVTrYwJ304.