English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Achieving long-term autonomy for mobile robots operating in real-worldunstructured environments such as farms remains a significant challenge. Thisis 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 mighthave to the robot's actions. Additionally, in order to achieve longer-termgoals, consideration of the limited on-board resources available to the robotis required, especially for extended missions such as weeding an agriculturalfield. To achieve efficient long-term autonomy, it is thus crucial tounderstand the impact that online dynamic updates to an energy efficientoffline plan might have on resource usage whilst navigating through crowds orherds. To address these challenges, a hierarchical planning framework isproposed, integrating an online local dynamic path planner with an offlinelonger-term objective-based planner. This framework acts to achieve long-termautonomy through awareness of both dynamic responses of individuals to arobot's motion and the limited resources available. This paper details thehierarchical approach and its integration on a robotic platform, including acomprehensive description of the planning framework and associated perceptionmodules. The approach is evaluated in real-world trials on farms, requiringboth consideration of limited battery capacity and the presence of nearbymoving individuals. These trials additionally demonstrate the ability of theframework to adapt resource use through variation of the local dynamic planner,allowing adaptive behaviour in changing environments. A summary video isavailable at https://youtu.be/DGVTrYwJ304.

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