{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:G6FCYXUI7XBAC6VJUUABXBN6AW","short_pith_number":"pith:G6FCYXUI","schema_version":"1.0","canonical_sha256":"378a2c5e88fdc2017aa9a5001b85be05ab34a2bb35d6def80b6c8319efd7dd99","source":{"kind":"arxiv","id":"2308.07974","version":1},"attestation_state":"computed","paper":{"title":"Neural-Network-Driven Method for Optimal Path Planning via High-Accuracy Region Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Cheng-Tien Tsao, Hee-hyol Lee, Tianyu Shen, Yuan Huang","submitted_at":"2023-08-15T18:23:29Z","abstract_excerpt":"Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2308.07974","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-08-15T18:23:29Z","cross_cats_sorted":[],"title_canon_sha256":"4c779b5523ee3cc3f46848ac822f67a372a139edd77eb6eb8c56cbfdc27815ad","abstract_canon_sha256":"a990e22540d54e689f99d1012440ee9da43b5aa9f9e36fdb8864d31f054697f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:41:52.486868Z","signature_b64":"ch6BYGcq7z34BO4LwxJDXZJmcXTPgLo/c/i5CqcFL2oqq3v+sDbMHIWbteGkQiL9XM3Xw/D1wbtX4Pk08P2WBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"378a2c5e88fdc2017aa9a5001b85be05ab34a2bb35d6def80b6c8319efd7dd99","last_reissued_at":"2026-07-05T06:41:52.486343Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:41:52.486343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural-Network-Driven Method for Optimal Path Planning via High-Accuracy Region Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Cheng-Tien Tsao, Hee-hyol Lee, Tianyu Shen, Yuan Huang","submitted_at":"2023-08-15T18:23:29Z","abstract_excerpt":"Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.07974","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.07974/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2308.07974","created_at":"2026-07-05T06:41:52.486410+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.07974v1","created_at":"2026-07-05T06:41:52.486410+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.07974","created_at":"2026-07-05T06:41:52.486410+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6FCYXUI7XBA","created_at":"2026-07-05T06:41:52.486410+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6FCYXUI7XBAC6VJ","created_at":"2026-07-05T06:41:52.486410+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6FCYXUI","created_at":"2026-07-05T06:41:52.486410+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00548","citing_title":"CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW","json":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW.json","graph_json":"https://pith.science/api/pith-number/G6FCYXUI7XBAC6VJUUABXBN6AW/graph.json","events_json":"https://pith.science/api/pith-number/G6FCYXUI7XBAC6VJUUABXBN6AW/events.json","paper":"https://pith.science/paper/G6FCYXUI"},"agent_actions":{"view_html":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW","download_json":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW.json","view_paper":"https://pith.science/paper/G6FCYXUI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.07974&json=true","fetch_graph":"https://pith.science/api/pith-number/G6FCYXUI7XBAC6VJUUABXBN6AW/graph.json","fetch_events":"https://pith.science/api/pith-number/G6FCYXUI7XBAC6VJUUABXBN6AW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW/action/storage_attestation","attest_author":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW/action/author_attestation","sign_citation":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW/action/citation_signature","submit_replication":"https://pith.science/pith/G6FCYXUI7XBAC6VJUUABXBN6AW/action/replication_record"}},"created_at":"2026-07-05T06:41:52.486410+00:00","updated_at":"2026-07-05T06:41:52.486410+00:00"}