Stamatopoulou, Maria;
Liu, Jianwei;
Kanoulas, Dimitrios;
(2024)
DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots.
In:
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(pp. pp. 7787-7793).
IEEE: Abu Dhabi, United Arab Emirates.
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Abstract
We present DiPPeST, a novel image and goal conditioned diffusion-based trajectory generator for quadrupedal robot path planning. DiPPeST is a zero-shot adaptation of our previously introduced diffusion-based 2D global trajectory generator (DiPPeR). The introduced system incorporates a novel strategy for local real-time path refinements, that is reactive to camera input, without requiring any further training, image processing, or environment interpretation techniques. DiPPeST achieves 92% success rate in obstacle avoidance for nominal environments and an average of 88% success rate when tested in environments that are up to 3.5 times more complex in pixel variation than DiPPeR. A visual-servoing framework is developed to allow for real-world execution, tested on the quadruped robot, achieving 80% success rate in different environments and showcasing improved behavior than complex state-of-the-art local planners, in narrow environments. Website: https://rpl-cs-ucl.github.io/DiPPeSTweb/
| Type: | Proceedings paper |
|---|---|
| Title: | DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots |
| Event: | 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Dates: | 14 Oct 2024 - 18 Oct 2024 |
| ISBN-13: | 979-8-3503-7770-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/IROS58592.2024.10802677 |
| Publisher version: | https://doi.org/10.1109/IROS58592.2024.10802677 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training; Navigation; Robot vision systems; Noise reduction; Generators; Real-time systems; Trajectory; Planning; Quadrupedal robots; Visual odometry |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203742 |
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