Towards a Unified Planner For Socially-Aware Navigation

This paper presents a novel architecture to attain a Unified Socially-Aware Navigation (USAN) and explains its need in Socially Assistive Robotics (SAR) applications. Our approach emphasizes interpersonal distance and how spatial communication can be used to build a unified planner for a human-robot...

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Bibliographic Details
Published inarXiv.org
Main Authors Santosh Balajee Banisetty, Feil-Seifer, David
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.10.2018
Subjects
Architecture
Artificial intelligence
Change detection
Machine learning
Navigation
Optimization
Personal communication
Robotics
Robots
Trajectory planning
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Summary:This paper presents a novel architecture to attain a Unified Socially-Aware Navigation (USAN) and explains its need in Socially Assistive Robotics (SAR) applications. Our approach emphasizes interpersonal distance and how spatial communication can be used to build a unified planner for a human-robot collaborative environment. Socially-Aware Navigation (SAN) is vital to make humans feel comfortable and safe around robots; HRI studies have shown the importance of SAN transcendents safety and comfort. SAN plays a crucial role in perceived intelligence, sociability and social capacity of the robot, thereby increasing the acceptance of the robots in public places. Human environments are very dynamic and pose serious social challenges to robots intended for interactions with people. For the robots to cope with the changing dynamics of a situation, there is a need to infer intent and detect changes in the interaction context. SAN has gained immense interest in the social robotics community; to the best of our knowledge, however, there is no planner that can adapt to different interaction contexts spontaneously after autonomously sensing the context. Most of the recent efforts involve social path planning for a single context. In this work, we propose a novel approach for a unified architecture to SAN that can plan and execute trajectories which are human-friendly for an autonomously sensed interaction context. Our approach augments the navigation stack of the Robot Operating System (ROS) utilizing machine learning and optimization tools. We modified the ROS navigation stack using a machine learning-based context classifier and a PaCcET based local planner for us to achieve the goals of USAN. We discuss our preliminary results and concrete plans on putting the pieces together in achieving USAN.
ISSN:2331-8422