aalto1 untyped-item.component.html
Learning temporal maps of dynamics for mobile robots
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Authors
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
Series
Robotics and Autonomous Systems, Volume 184
Abstract
Building a map representation of the surrounding environment is crucial for the successful operation of autonomous robots. While extensive research has concentrated on mapping geometric structures and static objects, the environment is also influenced by the movement of dynamic objects. Integrating information about spatial motion patterns in an environment can be beneficial for planning socially compliant trajectories, avoiding congested areas, and aligning with the general flow of people. In this paper, we introduce a deep state-space model designed to learn map representations of spatial motion patterns and their temporal changes at specific locations. Thus enabling the robot for human-compliant operation and improved trajectory forecasting in environments with evolving motion patterns. Validation of the proposed method is conducted using two datasets: one comprising generated motion patterns and the other featuring real-world pedestrian data. The model's performance is assessed in terms of learning capability, mapping quality, and its applicability to downstream robotics tasks. For comparative assessment of mapping quality, we employ CLiFF-Map as a baseline, and CLiFF-LHMP serves as another baseline for evaluating performance in downstream motion prediction tasks. The results demonstrate that our model can effectively learn corresponding motion patterns and holds promising potential for application in robotic tasks.
Description
Publisher Copyright: © 2024 The Authors
Other note
Citation
Shi, J & Kucner, T P 2025, 'Learning temporal maps of dynamics for mobile robots', Robotics and Autonomous Systems, vol. 184, 104853. https://doi.org/10.1016/j.robot.2024.104853