DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913368885952512 |
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| author | Stamatopoulou, Maria Liu, Jianwei Kanoulas, Dimitrios |
| author_facet | Stamatopoulou, Maria Liu, Jianwei Kanoulas, Dimitrios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19232 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots Stamatopoulou, Maria Liu, Jianwei Kanoulas, Dimitrios Robotics 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. |
| title | DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.19232 |