Shape Recognition for Industrial Robot Manipulation with the Bees Algorithm

Marco Castellani, Luca Baronti, Senjing Zheng, Feiying Lan

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Fitting primitive shapes to point cloud scenes is a challenging but neces-sary step for many robotic manipulation operations. State-of-the-art primitive fitting methods rely on geometric shape estimation or iterative procedures. They are often computationally complex and sensitive to al-gorithm parameterisation. This study tackles primitive fitting as a param-eter optimisation problem, solving it using the Bees Algorithm. The per-formance of the Bees Algorithm is evaluated on three sets of artificial scenes of varying degrees of blurriness and benchmarked against an evolutionary algorithm. Experimental results proved the precision and consistency of the Bees Algorithm. Primitive fitting times were compati-ble with real-time application.
Original languageEnglish
Title of host publicationIntelligent Manufacturing and Production Optimisation – The Bees Algorithm Approach
EditorsDuc Truong Pham, Natalia Hartono
PublisherSpringer Nature
Publication statusPublished - 2022

Publication series

NameEdit Springer Series in Advanced Manufacturing
PublisherSpringer
ISSN (Print)2196-1735

Fingerprint

Dive into the research topics of 'Shape Recognition for Industrial Robot Manipulation with the Bees Algorithm'. Together they form a unique fingerprint.

Cite this