{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IXYET6NVKTRN2N2PCNBEVELCQP","short_pith_number":"pith:IXYET6NV","schema_version":"1.0","canonical_sha256":"45f049f9b554e2dd374f13424a916283e6e2f2c5fd755e8abe94bc17f663aba5","source":{"kind":"arxiv","id":"2310.15931","version":1},"attestation_state":"computed","paper":{"title":"GO-FEAP: Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jiasong Zhu, Licong Zhuang, Weiye Zhang, Wenshuai Yu, Xiaoyi Zhang, Zhi Zeng","submitted_at":"2023-10-24T15:28:06Z","abstract_excerpt":"Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, are demonstrated to static local optima and two-dimensional exploration. To address these challenges, this paper introduces GO-FEAP (Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning), aiming to achieve efficient and complete three-dimensional exploration. Frontier-Omission-Aware Exploration module presented in this work takes into account multiple pivotal factors, encompassing frontier distance, nearby fronti"},"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":"2310.15931","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-10-24T15:28:06Z","cross_cats_sorted":[],"title_canon_sha256":"8b8db5592c2e87324c26e79ed2aea52d4954c38f043fe7ac1ee3371fd96b5d00","abstract_canon_sha256":"80ac1fc429a427d6f51287b3f27bfb6ea745b61564bf265c5ec484b7f5d0666e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:04:32.579626Z","signature_b64":"1+dOwPC/EIv4rb/XCRFr2WZIP9AsNXbIEvlqW35sV323y7Nt2wbcmfcz6Trl6+NrbCQ2yavxWlC+hg0GRSq6Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45f049f9b554e2dd374f13424a916283e6e2f2c5fd755e8abe94bc17f663aba5","last_reissued_at":"2026-07-05T07:04:32.579140Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:04:32.579140Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GO-FEAP: Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jiasong Zhu, Licong Zhuang, Weiye Zhang, Wenshuai Yu, Xiaoyi Zhang, Zhi Zeng","submitted_at":"2023-10-24T15:28:06Z","abstract_excerpt":"Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, are demonstrated to static local optima and two-dimensional exploration. To address these challenges, this paper introduces GO-FEAP (Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning), aiming to achieve efficient and complete three-dimensional exploration. Frontier-Omission-Aware Exploration module presented in this work takes into account multiple pivotal factors, encompassing frontier distance, nearby fronti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.15931","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/2310.15931/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":"2310.15931","created_at":"2026-07-05T07:04:32.579197+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.15931v1","created_at":"2026-07-05T07:04:32.579197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.15931","created_at":"2026-07-05T07:04:32.579197+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXYET6NVKTRN","created_at":"2026-07-05T07:04:32.579197+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXYET6NVKTRN2N2P","created_at":"2026-07-05T07:04:32.579197+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXYET6NV","created_at":"2026-07-05T07:04:32.579197+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP","json":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP.json","graph_json":"https://pith.science/api/pith-number/IXYET6NVKTRN2N2PCNBEVELCQP/graph.json","events_json":"https://pith.science/api/pith-number/IXYET6NVKTRN2N2PCNBEVELCQP/events.json","paper":"https://pith.science/paper/IXYET6NV"},"agent_actions":{"view_html":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP","download_json":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP.json","view_paper":"https://pith.science/paper/IXYET6NV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.15931&json=true","fetch_graph":"https://pith.science/api/pith-number/IXYET6NVKTRN2N2PCNBEVELCQP/graph.json","fetch_events":"https://pith.science/api/pith-number/IXYET6NVKTRN2N2PCNBEVELCQP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP/action/storage_attestation","attest_author":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP/action/author_attestation","sign_citation":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP/action/citation_signature","submit_replication":"https://pith.science/pith/IXYET6NVKTRN2N2PCNBEVELCQP/action/replication_record"}},"created_at":"2026-07-05T07:04:32.579197+00:00","updated_at":"2026-07-05T07:04:32.579197+00:00"}