Adaptive Path Planning for Reaching an Uncertain Set of Targets in a Fruit Tree

Werner Kroneman, Joao Valente, A. Frank Van Der Stappen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

When inspecting a set of target points (such as fruit) in a fruit tree, it is not realistic to assume that a perfectly-accurate set of such targets is available beforehand: compared to earlier inspections, new fruit may have appeared, and some fruit may be missing. In previous work, we introduced a shell-based method for rapid global path planning to position the end of a robotic arm close to numerous target points in an environment for inspection. In this work, we extend our method to handle partial knowledge of the set of targets, enabling the robot to adapt its planned path to newly-discovered or missing fruit on-the-fty. In the most extreme case, the robot starts with no knowledge of the workspace at all, except for what is immediately visible on startup. We show that, when not all targets are given beforehand, there is only a negligible increase in the length of the path traversed by the robot, the robot still effectively discovers a-priori unknown targets, at an overall about 35% increase in the CPU running time of the algorithm compared to the full-knowledge scenario.

Original languageEnglish
Title of host publication13th International Workshop on Robot Motion and Control, RoMoCo 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages111-116
Number of pages6
ISBN (Electronic)9798350393965
DOIs
Publication statusPublished - 29 Jul 2024
Event13th International Workshop on Robot Motion and Control, RoMoCo 2024 - Poznan, Poland
Duration: 2 Jul 20244 Jul 2024

Publication series

Name13th International Workshop on Robot Motion and Control, RoMoCo 2024 - Proceedings

Conference

Conference13th International Workshop on Robot Motion and Control, RoMoCo 2024
Country/TerritoryPoland
CityPoznan
Period2/07/244/07/24

Keywords

  • adaptive path planning
  • multi-goal
  • precision agriculture
  • UAV

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