Benchmark and Analysis of Path Planning Algorithms of “ROS MoveIt!” for Pick and Place Task in Tomato Harvesting

Jędrzejczyk, Filip; Bajer, Jarosław; Gawdzik, Grzegorz; Główka, Jakub; Sprońska, Agnieszka

doi:10.1007/978-3-030-74893-7_26

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the work makes the benchmark accessible to various related fields, from adaptive control and motion planning to learning the tasks through trial-and-error learning. Jedrzejczyk et al [10] have investigated a tomato harvesting application and suggested a benchmark of optimally configured motion planners available within Robot Operating System (ROS) and MoveIt platforms. The results indicated a comparison of efficiency and repeatability of particular planners for a planning scene imitating conditions in a greenhouse or similar pick-and-place tasks.…”

Section: A Motion Planning Benchmarkingmentioning

confidence: 99%

Benchmarking of Motion Planning Algorithms with Real-time 3D Occupancy Grid Map for an Agricultural Robotic Manipulator

Vaghefi,

Ibrahim,

Zaman

2022

IJACSA

View full text Add to dashboard Cite

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 highperforming algorithm and efficiently conducting such harvesting tasks in real practice.

show abstract

Section: A Motion Planning Benchmarkingmentioning

confidence: 99%