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1. Automatic Waypoint Generation to Improve Robot Navigation Through Narrow Spaces

Author

Javier Gonzalez-Jimenez, Jose-Raul Ruiz-Sarmiento, Cipriano Galindo, Javier Monroy, Francisco-Angel Moreno, Machine Perception and Intelligent Robotics Group (MAPIR), [Moreno,FA, Monroy,J, Ruiz-Sarmiento,JR, Galindo,C, Gonzalez-Jimenez,J] Machine Perception and Intelligent Robotics Group (MAPIR), Dept. of System Engineering and Automation Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, Spain., and This work has been supported by the research projects WISER (DPI2017-84827-R), funded by the Spanish Government and the European Regional Development’s Funds (FEDER), MoveCare (ICT-26-2016b-GA-732158), funded by the European H2020 program, and by a postdoc contract from the I-PPIT program of the University of Malaga. The publication of this paper has been funded by the University of Malaga.

Subjects
0209 industrial biotechnology, Traverse, Phenomena and Processes::Mathematical Concepts::Algorithms [Medical Subject Headings], Computer science, Chemicals and Drugs::Inorganic Chemicals::Free Radicals::Reactive Oxygen Species [Medical Subject Headings], waypoint generation, Real-time computing, Information Science::Information Science::Computing Methodologies::Artificial Intelligence::Robotics [Medical Subject Headings], 02 engineering and technology, lcsh:Chemical technology, Biochemistry, Article, Analytical Chemistry, Task (project management), Waypoint generation, Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings], Waypoint, robot deployment, 020901 industrial engineering & automation, Information Science::Information Science::Computing Methodologies::Artificial Intelligence [Medical Subject Headings], Navigation assistant, robot navigation, Artificial Intelligence, mobile robots, robot localization, 0202 electrical engineering, electronic engineering, information engineering, Mobile robots, Mapa, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, business.industry, Robot localization, Robotics, Mobile robot, Atomic and Molecular Physics, and Optics, Inteligencia artificial, Trajectory, Robot navigation, Map, Robot, 020201 artificial intelligence & image processing, Noise (video), Artificial intelligence, business, Robótica, Robot deployment
Abstract

In domestic robotics, passing through narrow areas becomes critical for safe and effective robot navigation. Due to factors like sensor noise or miscalibration, even if the free space is sufficient for the robot to pass through, it may not see enough clearance to navigate, hence limiting its operational space. An approach to facing this is to insert waypoints strategically placed within the problematic areas in the map, which are considered by the robot planner when generating a trajectory and help to successfully traverse them. This is typically carried out by a human operator either by relying on their experience or by trial-and-error. In this paper, we present an automatic procedure to perform this task that: (i) detects problematic areas in the map and (ii) generates a set of auxiliary navigation waypoints from which more suitable trajectories can be generated by the robot planner. Our proposal, fully compatible with the robotic operating system (ROS), has been successfully applied to robots deployed in different houses within the H2020 MoveCare project. Moreover, we have performed extensive simulations with four state-of-the-art robots operating within real maps. The results reveal significant improvements in the number of successful navigations for the evaluated scenarios, demonstrating its efficacy in realistic situations.

Published
2019
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2. Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Author

Sangseok You, Lionel P. Robert, Jeonghwan Kim, SangHyun Lee, Vineet R. Kamat, Okinawa Institute of Science and Technology Graduate University (OIST), Institute for Computational Engineering and Sciences [Austin] (ICES), University of Texas at Austin [Austin], and HEC Research Paper Series

Subjects
0209 industrial biotechnology, Identification, Computer science, 0211 other engineering and technologies, Theoretical models, 02 engineering and technology, Robot Acceptance Safety Model (RASM), Virtual reality, Trust, Human–robot interaction, 020901 industrial engineering & automation, Human–computer interaction, 021105 building & construction, 0502 economics and business, Set (psychology), Masonry, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Construction automation, Civil and Structural Engineering, Perceived safety, Immersive Virtual, business.industry, 05 social sciences, Robotics, Building and Construction, Environment (IVE), Human–Robot Work Collaboration (HRWC), Identification (information), Work (electrical), Control and Systems Engineering, Robot, [SHS.GESTION]Humanities and Social Sciences/Business administration, Intention to Work with Robot, Artificial intelligence, Safety, Immersive virtual environment, business, Humanoid robot, 050203 business & management, Virtual prototyping, Team
Abstract

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.

Published
2018