A Deep Learning Approach To Multi-Context Socially-Aware Navigation

We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorpor...

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Bibliographic Details
Published inarXiv.org
Main Authors Santosh Balajee Banisetty, Rajamohan, Vineeth, Vega, Fausto, Feil-Seifer, David
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.04.2021
Subjects
Classification
Concavity
Context
Decision making
Deep learning
Halls
Multiple objective analysis
Navigation
Norms
Pareto optimization
Robots
Subsystems
Trajectory planning
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Summary:We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially-appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot was able to select and account for social objectives related to navigation autonomously.
ISSN:2331-8422