Abstract: The performance evaluation of motion planning algorithms for agricultural robotic manipulators is commonly performed via benchmarking platforms. However, creating a realistic benchmarking scene that constrains the motion planning algorithms with the characteristic of a real-work environment has always been a challenge worthy of research. In this paper, we present a lab-setup benchmarking platform to evaluate Open Motion Planning Library (OMPL) motion planners for the application of a robotic harvester of a palm-like tree using a real-time 3D occupancy grid map. First, three motion problems were defined with different levels of complexity based on a real oil palm fruit harvesting task. To achieve reliable outcomes, the benchmarking scene was modeled by converting point cloud data from a stereo-depth sensor into a 3D occupancy grid map using the Octomap algorithm. Then the benchmarking was performed, all within a real-time process. According to the results, a fair performance evaluation was achieved by modeling a realistic benchmarking scene, which can help in choosing a high-performing algorithm and efficiently conducting such harvesting tasks in real practice.

Seyed Abdollah Vaghefi, Mohd Faisal Ibrahim and Mohd Hairi Mohd Zaman, “Benchmarking of Motion Planning Algorithms with Real-time 3D Occupancy Grid Map for an Agricultural Robotic Manipulator” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01306103

@article{Vaghefi2022,
title = {Benchmarking of Motion Planning Algorithms with Real-time 3D Occupancy Grid Map for an Agricultural Robotic Manipulator},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01306103},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01306103},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Seyed Abdollah Vaghefi and Mohd Faisal Ibrahim and Mohd Hairi Mohd Zaman}
}