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Rapid citrus harvesting motion planning with pre-harvesting point and quad-tree
[Display omitted] •Proposed a harvesting motion planning algorithm with a fast and high success rate.•Verified the algorithm by 2D and 3D simulations, in the lab and field environments.•Saved 40–60% of the path planning time by the algorithm.•Increased the path planning success rate to 95% in unstru...
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Published in: | Computers and electronics in agriculture 2022-11, Vol.202, p.107348, Article 107348 |
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Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | [Display omitted]
•Proposed a harvesting motion planning algorithm with a fast and high success rate.•Verified the algorithm by 2D and 3D simulations, in the lab and field environments.•Saved 40–60% of the path planning time by the algorithm.•Increased the path planning success rate to 95% in unstructured environment.
Aiming at the problems of low success rate and long planning time of harvesting robots in unstructured and complex environments, a harvesting motion planning algorithm based on pre-harvesting guide points (Pre-harvesting Guide Informed—RRT*, PGI-RRT*) is proposed. This algorithm is on the basis of progressively optimal informed RRT * algorithm. The third point between the starting point and the target point is selected as the pre-harvesting guide point. The four random trees are generated between the initial point, the target point and the guide point at the same time to quickly obtain the initial path. In the PGI-RRT* algorithm, P probability sampling is used to replace random sampling to improve the blindness of sampling. New nodes are generated by the dynamic step size. To do so can improve the speed and flexibility of the PGI-RRT* algorithm to explore the unknown space, and improve the convergence speed of the optimal path. 2D simulation experiments show that, compared with Informed-RRT*, the PGI-RRT* algorithm can reduce the time to find the initial path by about 75–86 %, and improve the success rate by 18–32 %. 3D simulation experiments and experiments in laboratory and field environments show that the PGI-RRT* algorithm proposed in this paper can significantly reduce the planning time and improve the planning success rate. Compared to the 9 algorithms added to the pre-harvesting guide points for double path planning in the OMPL, the PGI-RRT* algorithm can reduce the path planning time by 40–60 %, and improve the success rate of path planning to 95 %, and shorten the execution time of the manipulator by about 8 s. |
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ISSN: | 0168-1699 1872-7107 |
DOI: | 10.1016/j.compag.2022.107348 |