Robotic Crop Handling in Cluttered and Unstructured Environments using Simulated L-System Dynamic Plant Models

Abstract

This paper presents the development of a simulation for dynamic plant models, generated from L-system functional models. A key application of these dynamic plant models is to aid in developing new methods for robotic manipulation of plants that minimize damage due to physical interaction. We present a use case of the dynamic plant model by evaluating its performance against standard RRT and novel keyhole robot arm pruning algorithms in comparison with a physical plant. Through this paper, we show that the simulated plant model was able to predict the failure modes for each pruning algorithm. The dynamic plant model was also able to predict the performance difference between algorithms, simulated experiments predicting an increase in target point capture success rate from 57% RRT to 90% keyhole compared with 65% RRT to 86% keyhole when applied to a physical sample; thus validating it as a useful simulation tool for developing and testing novel robotic methods for plant handling within cluttered and unstructured environments.