Citation: Zhang, H.; Ge, Y.; Sun, C.;
Zeng, H.; Liu, N. Picking Path
Planning Method of Dual Rollers
Type Safflower Picking Robot Based
on Improved Ant Colony Algorithm.
Processes 2022, 10, 1213. https://
doi.org/10.3390/pr10061213
Academic Editors: Kelvin K.L. Wong,
Dhanjoo N. Ghista, Andrew W.H. Ip
and Wenjun (Chris) Zhang
Received: 26 April 2022
Accepted: 15 June 2022
Published: 17 June 2022
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Article
Picking Path Planning Method of Dual Rollers Type Safflower
Picking Robot Based on Improved Ant Colony Algorithm
He Zhang, Yun Ge *, Chao Sun, Haifeng Zeng and Na Liu
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China;
zhanghe@stu.shzu.edu.cn (H.Z.); sunchao@stu.shzu.edu.cn (C.S.); zenghaifeng@shzu.edu.cn (H.Z.);
liuna0927@shzu.edu.cn (N.L.)
* Correspondence: gy_mac@shzu.edu.cn
Abstract:
Aiming at the problem of automatic path planning for the whole safflower bulbs during
the operation of safflower picking robots, an improved ant colony algorithm (ACA) was proposed
to plan the three-dimensional path of the safflower picking points. The shortest time and distance
were taken as the overall goal of path planning to comprehensively improve the working efficiency
of safflower picking robots. First, in order to shorten time, the angle induction factor was introduced
to reduce the angle rotation of the end-effector. Second, in order to shorten the length of the picking
path, the picking track was optimized. Finally, the design of the secondary path optimization reduced
the number of picking points, which not only shortened the length of the picking path, but also
shortened the picking time. The simulation results show that the path planned by the improved ACA
was reduced by three picking points, shortening the total length by 74.32%, and reducing the picking
time by 0.957 s. The simulation results verify the feasibility of the improved ACA for safflower
picking path planning, which provides theoretical reference and technical support for the picking
path planning of dual roller safflower picking robots.
Keywords: robot; ant colony algorithm; path planning; safflower; picking track
1. Introduction
Safflower combines pharmacology, dyes and oils, and has high economic value [
1
].
Safflower plants are spatially distributed, one branch grows one bulb, and the ripening
time is different, so safflower needs to be harvested selectively [
2
]. Considering that the
safflower picking robot picks safflower filaments in the field, the energy consumption
should be reduced as much as possible. Due to the short flowering period of safflower,
the filaments become dry and hard after the sixth day of safflower opening, which affects
the effect of picking safflower filaments by the dual rollers type end-effector. In order to
reduce the energy consumption of safflower picking robots and ensure the high net picking
rate and low dropping rate of safflower filaments, the working efficiency of safflower
picking robots should be improved as much as possible. Therefore, the planned picking
path should avoid the end-effector repeatedly passing through the same picking point and
wasting more unnecessary energy. It is required to finish picking the whole safflower bulbs
with the shortest path length and picking time to improve the working efficiency of the
safflower picking robot. Therefore, the path planning of the safflower picking robot is a
travelling salesman problem (TSP). In conclusion, in order to improve the picking efficiency
of the dual rollers type safflower picking robot, this paper explores a picking path planning
method suitable for the dual rollers type safflower picking robot with the overall goal of
minimum time and shortest distance.
In order to improve the working efficiency of the robot, two methods are usually used
to optimize work paths: One is to optimize work paths by sequencing them with a certain
algorithm, that is, for a certain work goal or multiple goals of the robot, the work points
are sequenced according to certain evaluation criteria to arrive at the optimal operation
Processes 2022, 10, 1213. https://doi.org/10.3390/pr10061213 https://www.mdpi.com/journal/processes
