Online Coverage Planning for an Autonomous Weed Mowing Robot with Curvature Constraints

Parikshit Maini, Burak M. Gonultas, Volkan Isler

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Thearea used for grazing cattle constitutes about one-third of the land in United States. In this paper, we present the design of Cowbot, an autonomous weed mowing robot for maintaining cow pastures. Cowbot is designed to operate in rugged environments and provides a cost-effective method for weed control in cow pastures. Path planning for the Cowbot is challenging since weed distribution on pastures is unknown. Given an onboard sensor with a limited field of view, online planning is necessary to detect weeds and plan paths to mow them. We study the general online coverage planning problem for an autonomous mower with curvature and field of view constraints. We develop two algorithms that are able to utilize new information on weed detection to optimize path length and ensure coverage. We deploy our algorithms on the Cowbot and perform field experiments to validate the suitability of our methods for real-time path planning. We also perform extensive simulation experiments which show that our algorithms result in up to 60% reduction in path length as compared to boustrophedon and random-search based coverage paths.

Original languageEnglish (US)
Pages (from-to)5445-5452
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 1 2022

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • Agricultural Automation
  • Cows
  • Field Robots
  • Materials requirements planning
  • Motion and Path Planning
  • Nonholonomic Motion Planning
  • Path planning
  • Planning
  • Robot sensing systems
  • Robots
  • Routing
  • Field robots
  • Agricultural automation
  • Nonholonomic motion planning
  • Motion and path planning


Dive into the research topics of 'Online Coverage Planning for an Autonomous Weed Mowing Robot with Curvature Constraints'. Together they form a unique fingerprint.

Cite this