Research on the local path planning of an orchard mowing robot based on an elliptic repulsion scope boundary constraint potential field method

Abstract

In orchard scenes, the complex terrain environment will affect the operational safety of mowing robots. For this reason, this paper proposes an improved local path planning algorithm for an artificial potential field, which introduces the scope of an elliptic repulsion potential field as the boundary potential field. The potential field function adopts an improved variable polynomial and adds a distance factor, which effectively solves the problems of unreachable targets and local minima. In addition, the scope of the repulsion potential field is changed to an ellipse, and a fruit tree boundary potential field is added, which effectively reduces the environmental potential field complexity, enables the robot to avoid obstacles in advance without crossing the fruit tree boundary, and improves the safety of the robot when working independently. The path length planned by the improved algorithm is 6.78% shorter than that of the traditional artificial potential method, The experimental results show that the path planned using the improved algorithm is shorter, smoother and has good obstacle avoidance ability.

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm

“…(2023) proposed an ant colony optimization algorithm to solve multitarget waypoint planning for unmanned aerial vehicles in orchards. Zhang et al. (2023) introduced an improved artificial potential field algorithm for weed-removal robot path planning, improving the safety of robot automation operations.…”

“…Tian et al (2023) proposed an ant colony optimization algorithm to solve multitarget waypoint planning for unmanned aerial vehicles in orchards. Zhang et al (2023) introduced an improved artificial potential field algorithm for weed-removal robot path planning, improving the safety of robot automation operations. Zhang et al (2022) conducted LiDAR scans of greenhouse maps and employed the probabilistic roadmap technique to calculate the paths between all target points, consequently establishing comprehensive global paths for navigating multiple operational points.…”