Robotic Lime Picking by Considering Leaves as Permeable Obstacles

TL;DR

This paper presents a novel robotic lime-picking method that models dense foliage as permeable obstacles with collision costs, improving path planning efficiency and success in real-world fruit harvesting scenarios.

Contribution

It introduces an adapted RRT* algorithm incorporating leaf permeability and collision costs, along with an APF bias to enhance path planning for robotic fruit picking.

Findings

Proposed method outperforms prior approaches in simulation.

Effective in real-world lime harvesting tasks.

Reduces planning time with APF bias.

Abstract

The problem of robotic lime picking is challenging; lime plants have dense foliage which makes it difficult for a robotic arm to grasp a lime without coming in contact with leaves. Existing approaches either do not consider leaves, or treat them as obstacles and completely avoid them, often resulting in undesirable or infeasible plans. We focus on reaching a lime in the presence of dense foliage by considering the leaves of a plant as 'permeable obstacles' with a collision cost. We then adapt the rapidly exploring random tree star (RRT*) algorithm for the problem of fruit harvesting by incorporating the cost of collision with leaves into the path cost. To reduce the time required for finding low-cost paths to goal, we bias the growth of the tree using an artificial potential field (APF). We compare our proposed method with prior work in a 2-D environment and a 6-DOF robot simulation. Our experiments and a real-world demonstration on a robotic lime picking task demonstrate the applicability of our approach.