Silva, Steven, Romero-Cano, Victor  ORCID: https://orcid.org/0000-0003-2910-5116 and Hernandez, Juan David ORCID: https://orcid.org/0000-0002-9593-6789
2026.
Social robot navigation under kinodynamic constraints using learning-informed sampling for indoor environments.
IEEE Robotics and Automation Letters
11
(7)
, pp. 9000-9007.
10.1109/LRA.2026.3699250
|
Preview |
PDF
- Accepted Post-Print Version
Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
With the inclusion of robots in social spaces to assist in service tasks (e.g. waitering and guiding people), having robots that move in a socially acceptable manner has become a requirement. In such dynamic environments, robots might show sudden stops and unexpected changes in acceleration, which can negatively affect the robots’ predictability and legibility. Previous work has attempted to reduce such acceleration changes by generating paths that are both kinodynamically feasible and socially acceptable. However, most of these approaches not only fail to guarantee path optimality but also involve a computational cost that limits the robot’s online navigation capabilities. In this paper, we present a social robot navigation (SRN) framework designed to overcome these limitations through three blocks: 1) world representation, 2) multilayered path planning, and 3) path-following control. Our approach incorporates a CNN-driven informed sampling strategy, which improves the robot’s path planner performance to meet online computation constraints. We extensively benchmark our framework against state-of-the-art approaches in a variety of simulated scenarios, and demonstrate its feasibility with the Reachy robot in real-world tests.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Additional Information: | RRS applied |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 2377-3766 |
| Date of First Compliant Deposit: | 14 May 2026 |
| Date of Acceptance: | 1 May 2026 |
| Last Modified: | 05 Aug 2026 09:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/187006 |
Actions (repository staff only)
 |
Edit Item |
Altmetric
Altmetric