{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-novel-learning-based-global-path-planning","title":"A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers","arxiv_id":"1811.10437","date":"2018-11-23","proceeding":null,"authors":["Jiang Zhang","Yuanqing Xia","Ganghui Shen"],"abstract":"Autonomous path planning algorithms are significant to planetary exploration\nrovers, since relying on commands from Earth will heavily reduce their\nefficiency of executing exploration missions. This paper proposes a novel\nlearning-based algorithm to deal with global path planning problem for\nplanetary exploration rovers. Specifically, a novel deep convolutional neural\nnetwork with double branches (DB-CNN) is designed and trained, which can plan\npath directly from orbital images of planetary surfaces without implementing\nenvironment mapping. Moreover, the planning procedure requires no prior\nknowledge about planetary surface terrains. Finally, experimental results\ndemonstrate that DB-CNN achieves better performance on global path planning and\nfaster convergence during training compared with the existing Value Iteration\nNetwork (VIN).","url_abs":"http://arxiv.org/abs/1811.10437v1","url_pdf":"http://arxiv.org/pdf/1811.10437v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-novel-learning-based-global-path-planning","repo_url":"https://github.com/bitzj2015/DB-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}