TY - CONF
T1 - Collision free path planning of a robotic manipulator for pruning apple trees
AU - Zahid, Azlan
AU - He, Long
AU - Choi, Daeun Dana
AU - Schupp, James
AU - Heinemann, Paul
N1 - Funding Information:
This research was partially supported in part by United States Department of Agriculture (USDA)'s National Institute of Food and Agriculture Federal Appropriations under Project PEN04547 and Accession No. 1001036. We also would like to give our special thanks for the support from Penn State College of Agricultural Sciences Stoy G. and Della E. Sunday program and Northeast Sustainable Agriculture Research and Education (SARE) Graduate Student Grant GNE19-225-33243.
Funding Information:
This research was partially supported in part by United States Department of Agriculture (USDA)’s National Institute of Food and Agriculture Federal Appropriations under Project PEN04547 and Accession No. 1001036. We also would like to give our special thanks for the support from Penn State College of Agricultural Sciences Stoy G. and Della E. Sunday program and Northeast Sustainable Agriculture Research and Education (SARE) Graduate Student Grant GNE19-225-33243.
Publisher Copyright:
© ASABE 2020 Annual International Meeting.
PY - 2020
Y1 - 2020
N2 - Pruning of apple trees requires 80-120 working hours of labor per hectare accounting for 20% of the total production cost. Robotic pruning is a potential solution to decrease labor dependence and associated costs. Autonomous precise manipulation of a robotic manipulator in presence of obstacles is a challenge. The spatial requirements and collision-free path planning for the robotic manipulator is essential for automated systems. This simulation study focused on investigating the branch accessibility of a six-rotational (6R) degrees of freedom (DoF) robotic manipulator with a shear blade type end-effector. A virtual tree canopy environment was established in MATLAB for simulation. The Rapidly-exploring Random Tree (RRT) obstacle avoidance algorithm was used to establish a collision-free path to reach the target pruning points. The path smoothing and optimization algorithms were also used to reduce path length and calculate the optimize path. The simulation showed that the integrated robotic manipulator reached the pruning points avoiding obstacle untargeted branches. The path generation time, path length, target reaching time, and number of accessible branches (success) and collisions (failure) was recorded. The study provides the foundation information for future work on the development of a robotic pruning system.
AB - Pruning of apple trees requires 80-120 working hours of labor per hectare accounting for 20% of the total production cost. Robotic pruning is a potential solution to decrease labor dependence and associated costs. Autonomous precise manipulation of a robotic manipulator in presence of obstacles is a challenge. The spatial requirements and collision-free path planning for the robotic manipulator is essential for automated systems. This simulation study focused on investigating the branch accessibility of a six-rotational (6R) degrees of freedom (DoF) robotic manipulator with a shear blade type end-effector. A virtual tree canopy environment was established in MATLAB for simulation. The Rapidly-exploring Random Tree (RRT) obstacle avoidance algorithm was used to establish a collision-free path to reach the target pruning points. The path smoothing and optimization algorithms were also used to reduce path length and calculate the optimize path. The simulation showed that the integrated robotic manipulator reached the pruning points avoiding obstacle untargeted branches. The path generation time, path length, target reaching time, and number of accessible branches (success) and collisions (failure) was recorded. The study provides the foundation information for future work on the development of a robotic pruning system.
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U2 - 10.13031/aim.202000439
DO - 10.13031/aim.202000439
M3 - Paper
AN - SCOPUS:85094316690
T2 - 2020 ASABE Annual International Meeting
Y2 - 13 July 2020 through 15 July 2020
ER -