Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Multiple human trajectory prediction and cooperative navigation modeling in crowded scenes
Date
2020-07-01
Author
Hacinecipoglu, Akif
Konukseven, Erhan İlhan
Koku, Ahmet Buğra
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
281
views
0
downloads
Cite This
As mobile robots start operating in environments crowded with humans, human-aware navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear route leading to it. There are significant efforts to solve this problem for mobile robots; however, they are not scalable to high human density and learning based approaches depend heavily on the context and configuration of the set they are trained with. We develop a method which infers initial trajectories from Gaussian processes and updates these trajectories jointly for all agents using a cost based interaction approach. We condition Gaussian processes online with the best hypothesis at each step of prediction horizon. The method is tested on a common public dataset and it is shown that it outperforms two state-of-the-art approaches in terms of human-likeness of predicted trajectories.
Subject Keywords
Mechanical Engineering
,
Engineering (miscellaneous)
,
Artificial Intelligence
,
Computational Mechanics
URI
https://hdl.handle.net/11511/35833
Journal
INTELLIGENT SERVICE ROBOTICS
DOI
https://doi.org/10.1007/s11370-020-00333-8
Collections
Department of Mechanical Engineering, Article
Suggestions
OpenMETU
Core
Human aware navigation of a mobile robot in crowded dynamic environments
Hacınecipoğlu, Akif; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2019)
As mobile robots start operating in dynamic environments crowded with humans, human-aware and human-like navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People can navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear path leading to it. This is clearly a dexterity of humans. But the mobile robots which have to navigate in such environments lack this feature. Even perf...
Multi-agent system-based fuzzy controller design with genetic tuning for a mobile manipulator robot in the hand over task
Erden, MS; Leblebicioğlu, Mehmet Kemal; Halıcı, Uğur (Springer Science and Business Media LLC, 2004-03-01)
This paper presents an application of the multi-agent system approach to a service mobile manipulator robot that interacts with a human during an object delivery and hand-over task in two dimensions. The base, elbow and shoulder of the robot are identified as three different agents, and are controlled using fuzzy control. The control variables of the controllers are linear velocity of the base, angular velocity of the elbow, and angular velocity of the shoulder. Main inputs to the system are the horizontal ...
Simultaneous localization and mapping for a mobile robot operating in outdoor environments
Sezginalp, Emre; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2007)
In this thesis, a method to the solution of autonomous navigation problem of a robot working in an outdoor application is sought. The robot will operate in unknown terrain where there is no a priori map present, and the robot must localize itself while simultaneously mapping the environment. This is known as Simultaneous Localization and Mapping (SLAM) problem in the literature. The SLAM problem is attempted to be solved by using the correlation between range data acquired at different poses of the robot. A...
Design of an image acquisition setup for mimic tracking
Aköner, Özgüler Mine; Koku, Ahmet Buğra; Department of Mechanical Engineering (2007)
With the advances in computer technology and the changing needs of people’s daily lives, robots start to offer alternative solutions. As one of these solutions, the branch of humanoid robots emerged as advanced robots that can interact with people. Robot faces are one of the most effective means of interacting with people; since they can express their emotions and reactions through facial mimics. However, the development of realistic robot faces necessitates the knowledge of the trajectories and displacemen...
Design of a mobile robot to move on rough terrain
Kırmızıgül, Uğur; İder, S. Kemal; Department of Mechanical Engineering (2005)
In this thesis work, a mobile robot is designed to be used in search and rescue operations to help the human rescue workers. The difficult physical conditions in the ruins obstruct the movement. Therefore, it is aimed to design a search and rescue robot which can move easily on rough terrain and climb over the obstacles. The designed robot is made up of three modules. A connecting unit is designed that is situated between each module. This connecting unit which is composed of two universal and one revolute ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. Hacinecipoglu, E. İ. Konukseven, and A. B. Koku, “Multiple human trajectory prediction and cooperative navigation modeling in crowded scenes,”
INTELLIGENT SERVICE ROBOTICS
, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35833.