TY - GEN
T1 - Adaptive Path Planning for Reaching an Uncertain Set of Targets in a Fruit Tree
AU - Kroneman, Werner
AU - Valente, Joao
AU - Van Der Stappen, A. Frank
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/7/29
Y1 - 2024/7/29
N2 - 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.
AB - 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.
KW - adaptive path planning
KW - multi-goal
KW - precision agriculture
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85201179123&partnerID=8YFLogxK
U2 - 10.1109/RoMoCo60539.2024.10604386
DO - 10.1109/RoMoCo60539.2024.10604386
M3 - Conference contribution
AN - SCOPUS:85201179123
T3 - 13th International Workshop on Robot Motion and Control, RoMoCo 2024 - Proceedings
SP - 111
EP - 116
BT - 13th International Workshop on Robot Motion and Control, RoMoCo 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Workshop on Robot Motion and Control, RoMoCo 2024
Y2 - 2 July 2024 through 4 July 2024
ER -