Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.15715 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908275968049152 |
|---|---|
| author | Takamido, Ryota Ota, Jun |
| author_facet | Takamido, Ryota Ota, Jun |
| contents | This study aims to address the key challenge of obtaining a high-quality solution path within a short calculation time by generalizing a limited dataset. In the informed experience-driven random trees connect star (IERTC*) process, the algorithm flexibly explores the search trees by morphing the micro paths generated from a single experience while reducing the path cost by introducing a re-wiring process and an informed sampling process. The core idea of this algorithm is to apply different strategies depending on the complexity of the local environment; for example, it adopts a more complex curved trajectory if obstacles are densely arranged near the search tree, and it adopts a simpler straight line if the local environment is sparse. The results of experiments using a general motion benchmark test revealed that IERTC* significantly improved the planning success rate in difficult problems in the cluttered environment (an average improvement of 49.3% compared to the state-of-the-art algorithm) while also significantly reducing the solution cost (a reduction of 56.3%) when using one hundred experiences. Furthermore, the results demonstrated outstanding planning performance even when only one experience was available (a 43.8% improvement in success rate and a 57.8% reduction in solution cost). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experience-based Optimal Motion Planning Algorithm for Solving Difficult Planning Problems Using a Limited Dataset Takamido, Ryota Ota, Jun Robotics This study aims to address the key challenge of obtaining a high-quality solution path within a short calculation time by generalizing a limited dataset. In the informed experience-driven random trees connect star (IERTC*) process, the algorithm flexibly explores the search trees by morphing the micro paths generated from a single experience while reducing the path cost by introducing a re-wiring process and an informed sampling process. The core idea of this algorithm is to apply different strategies depending on the complexity of the local environment; for example, it adopts a more complex curved trajectory if obstacles are densely arranged near the search tree, and it adopts a simpler straight line if the local environment is sparse. The results of experiments using a general motion benchmark test revealed that IERTC* significantly improved the planning success rate in difficult problems in the cluttered environment (an average improvement of 49.3% compared to the state-of-the-art algorithm) while also significantly reducing the solution cost (a reduction of 56.3%) when using one hundred experiences. Furthermore, the results demonstrated outstanding planning performance even when only one experience was available (a 43.8% improvement in success rate and a 57.8% reduction in solution cost). |
| title | Experience-based Optimal Motion Planning Algorithm for Solving Difficult Planning Problems Using a Limited Dataset |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.15715 |