Machine learning for the perception of autonomous construction machinery

Abstract

Abstract Robotic systems require holistic capabilities to sense, perceive, and act autonomously within their application environment. A safe and trustworthy autonomous operation is essential, especially in hazardous environments and critical applications like autonomous construction machinery for the decontamination of landfill sites. This article presents an enhanced combination of machine learning (ML) methods with classic artificial intelligence (AI) methods and customized validation methods to ensure highly reliable and accurate sensing and perception of the environment for autonomous construction machinery. The presented methods have been developed, evaluated, and applied within the Competence Center »Robot Systems for Decontamination in Hazardous Environments« (ROBDEKON) for investigating and developing robotic systems for autonomous decontamination tasks. The objective of this article is to give a holistic, in-depth overview for the ML-based part of the perception pipeline for an autonomous construction machine working in unstructured environments.