| Issue |
EPJ Web Conf.
Volume 355, 2026
4th International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics (STAAAR 2025)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 17 | |
| Section | Robotics, Exoskeletons and AI Modeling | |
| DOI | https://doi.org/10.1051/epjconf/202635501005 | |
| Published online | 03 March 2026 | |
https://doi.org/10.1051/epjconf/202635501005
Development and evaluation of obstacle detection mechanisms for autonomous aerial systems
Department of Robotics and Automation, Rajalakshmi Engineering College, Chennai 602105, Tamil Nadu, India
* Corresponding Author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 3 March 2026
Abstract
Autonomous robots rely heavily on avoidance algorithms and do not typically use obstacle contact detection, which limits their usefulness in congested situations. Blind spots or discretized detection locations plague current contact-detection systems, and performance is further limited by stiff platforms that only detect collisions without offering meaningful feedback. We offer a novel architecture for contact sensors that enhances autonomous navigation by detecting physical contact as a solution to these problems. The system uses a flex-sensor-equipped elastic collision platform to measure displacements in the event of a collision. Data collected by flex sensors is transformed into useful contact information by a contact-detection algorithm that were based on a neural network. By utilizing sensor data for real-time collision recovery, collision system was tested with collisions that occurred during both autonomous contact-based missions and manual flights. The testing findings showed that the system could accurately detect collision parameters and contact events estimate, even in scenarios when the parameters were changing. Future research on contact-based navigation systems can build on the suggested methodology, which offers a strong method for improving autonomous navigation in complicated surroundings.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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