Performance of a low-cost, human-inspired perception approach for dense moving crowd navigation | IEEE Conference Publication | IEEE Xplore

Performance of a low-cost, human-inspired perception approach for dense moving crowd navigation


Abstract:

We describe a method for low-cost awareness of characteristics of dense, moving crowds such as group formation, personal space approximation, and occlusion compensation f...Show More

Abstract:

We describe a method for low-cost awareness of characteristics of dense, moving crowds such as group formation, personal space approximation, and occlusion compensation for use in navigating through crowds. It incorporates social expectations and is inspired by human perceptual processes. The approach uses a single Kinect to cluster all moving objects into groups, applies a 2D polygon projection in obscured regions, and a group personal space modeled using asymmetric Gaussians in order to inhibit certain socially inappropriate robot paths. This approach trades off detection of individual people for higher coverage and lower cost, while preserving high speed processing. A real-world evaluation of this approach showed good performance in comparison to an existing people detection approach. The projected polygon step captures significantly more people in the scene (77% vs. 80%) and supports group clustering in dense, complex scenarios. Examples are provided for group splitting and merging, dense crowds with obstructions, and cases where other approaches typically encounter difficulty.
Date of Conference: 26-31 August 2016
Date Added to IEEE Xplore: 17 November 2016
ISBN Information:
Electronic ISSN: 1944-9437
Conference Location: New York, NY, USA
Citations are not available for this document.

I. Introduction

As mobile robots become more common in society their novelty and entertainment value will reduce, thus creating new challenges for robot navigation. Currently, independent robot travel produces unnatural crowd dynamics. Bystanders unfamiliar with robots are usually careful, suspicious, and concerned for their safety, thereby creating large spaces for robots to move through. Similarly, inappropriate or rude behavior by the robot is tolerated due to novelty. Asocial methods like linear “robotic” travel are usually effective but create a nuisance, leading to resentment and sometimes undesirable contact and malicious actions by nearby humans (e.g., [1]). Therefore, robots will soon need to travel in crowds in more socially appropriate methods.

Cites in Papers - |

Cites in Papers - IEEE (2)

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1.
Allan Wang, Aaron Steinfeld, "Group Split and Merge Prediction With 3D Convolutional Networks", IEEE Robotics and Automation Letters, vol.5, no.2, pp.1923-1930, 2020.
2.
Marynel Vázquez, Elizabeth J. Carter, Braden McDorman, Jodi Forlizzi, Aaron Steinfeld, Scott E. Hudson, "Towards Robot Autonomy in Group Conversations: Understanding the Effects of Body Orientation and Gaze", 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI, pp.42-52, 2017.

Cites in Papers - Other Publishers (8)

1.
Viktor Schmuck, Oya Celiktutan, "SANG: Socially Aware Navigation Between Groups", International Journal of Social Robotics, 2025.
2.
Sumin Kang, Sungwoo Yang, Daewon Kwak, Yura Jargalbaatar, Donghan Kim, "Social Type-Aware Navigation Framework for Mobile Robots in Human-Shared Environments", Sensors, vol.24, no.15, pp.4862, 2024.
3.
Christoforos Mavrogiannis, Francesca Baldini, Allan Wang, Dapeng Zhao, Pete Trautman, Aaron Steinfeld, Jean Oh, "Core Challenges of Social Robot Navigation: A Survey", ACM Transactions on Human-Robot Interaction, vol.12, no.3, pp.1, 2023.
4.
Yotaro Fuse, Masataka Tokumaru, "Personal Space Norms Aware Robotic Navigation Model and Its Evaluation in a Virtual Reality Environment", HCI International 2021 - Posters, vol.1420, pp.123, 2021.
5.
Stefano Seriani, Luca Marcini, Matteo Caruso, Paolo Gallina, Eric Medvet, "Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization", Journal of Intelligent & Robotic Systems, vol.101, no.2, 2021.
6.
Yotaro Fuse, Hiroshi Takenouchi, Masataka Tokumaru, Soft Computing for Biomedical Applications and Related Topics, vol.899, pp.117, 2021.
7.
Yotaro Fuse, Masataka Tokumaru, "Navigation Model for a Robot as a Human Group Member to Adapt to Changing Conditions of Personal Space", Journal of Advanced Computational Intelligence and Intelligent Informatics, vol.24, no.5, pp.621, 2020.
8.
Marynel Vázquez, Elizabeth J. Carter, Braden McDorman, Jodi Forlizzi, Aaron Steinfeld, Scott E. Hudson, "Towards Robot Autonomy in Group Conversations", Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp.42, 2017.

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