Robotic Lime Picking by Considering Leaves as Permeable Obstacles

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.

Literature study on robotic tree harvesting and pruning : The Next Fruit 4.0 - Multifunctional robot deliverable 1

“…Most robotic harvesting systems make use of robotic arms or a linear system that gives the end-effector at least 6 degrees of freedom (Bac et al, 2014;Roldán et al, 2018). For the path planning most studies rely on rapid exploring random trees (RRT) (Nemlekar et al, 2021), which they combine with a method to check whether the robot is in collision. For this the Flexible Collision Library (Pan et al, 2012) is used, but also collision detectors based on geometric primitives are used (Paulin et al, 2015) as they are computationally cheaper than complex meshes.…”