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Toward Autonomous Robots for Demolitions in Unstructured Environments

  • Francesco Corucci
  • Emanuele Ruffaldi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

The construction industry is a capital-intensive sector that is steadily turning toward mechanized and automated solutions in the past few decades. However, due to some specificities of this field, it is still technologically behind other sectors, such as manufacturing. Robotic technologies provide room for improvements, that could lead to economical, technical, and also social benefits. We present a possible conceptual framework for an autonomous robot for indoor demolitions, featuring enhanced perceptual capabilities, situational awareness, as well as intuitive Human–Robot Interaction (HRI) paradigms. The paper deals with several aspects of the demolition task, ranging from perception, to planning, to HRI. With respect to perception, we focus on the design and development of some of the perceptual capabilities needed for such robots, as essential to support autonomy, safety, and situational awareness in unstructured construction sites. Particularly, we propose a novel segmentation algorithm that allows the robot to work in highly unstructured scenarios, as well as a mechanism for detecting and quantifying spatial changes during the task. As far as HRI is concerned, a novel interaction paradigm based on laser designation is proposed. Proposed concepts were implemented and tested on a real, scaled-down, controlled mock-up that, while simplifying some aspects of the task, is able to mimic some general characteristics of a real demolition scenario. From lessons learned in this controlled environment we point out some requirements and foreseen issues in facing the complexity of a real demolition setup.

Keywords

Point Cloud Segmentation Algorithm Segmentation Plane Situational Awareness Planning Module 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The BioRobotics InstituteScuola Superiore Sant’AnnaPisaItaly
  2. 2.PERCRO, TeCIP Institute, Scuola Superiore Sant’AnnaPisaItaly

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