{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IIDCKZDL4IDK2SJQQNGUUMSGIU","short_pith_number":"pith:IIDCKZDL","schema_version":"1.0","canonical_sha256":"420625646be206ad4930834d4a3246452de80d9623fc4ca5078b3f7481d58089","source":{"kind":"arxiv","id":"1903.11750","version":2},"attestation_state":"computed","paper":{"title":"Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Allison O'Connell, Hunter Damron, Ioannis Rekleitis, James Johnson, Jason M. O'Kane, Marios Xanthidis, Nare Karapetyan, Sharmin Rahman","submitted_at":"2019-03-28T01:27:10Z","abstract_excerpt":"Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in \"fly-overs\" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through "},"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":"1903.11750","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-28T01:27:10Z","cross_cats_sorted":[],"title_canon_sha256":"2ac73aef017536176faad593e97c3b16371193cd2a0fc35cfd162cd4852bc78f","abstract_canon_sha256":"549fa2a85ce525fa4b64515b0b5ed798f9a348e31e7d5b83aaf7c30129d91649"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:01.488813Z","signature_b64":"ezSOZ6w4G+oj+BKeqBtLTxnQKMIkWXZ4NI2aIK4T3FzTu2ylsKozr5h15+i7WTXukTLreDMBxF1VarwFi32lDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"420625646be206ad4930834d4a3246452de80d9623fc4ca5078b3f7481d58089","last_reissued_at":"2026-07-05T01:02:01.488298Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:01.488298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Allison O'Connell, Hunter Damron, Ioannis Rekleitis, James Johnson, Jason M. O'Kane, Marios Xanthidis, Nare Karapetyan, Sharmin Rahman","submitted_at":"2019-03-28T01:27:10Z","abstract_excerpt":"Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in \"fly-overs\" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11750","kind":"arxiv","version":2},"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/1903.11750/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":"1903.11750","created_at":"2026-07-05T01:02:01.488381+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.11750v2","created_at":"2026-07-05T01:02:01.488381+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11750","created_at":"2026-07-05T01:02:01.488381+00:00"},{"alias_kind":"pith_short_12","alias_value":"IIDCKZDL4IDK","created_at":"2026-07-05T01:02:01.488381+00:00"},{"alias_kind":"pith_short_16","alias_value":"IIDCKZDL4IDK2SJQ","created_at":"2026-07-05T01:02:01.488381+00:00"},{"alias_kind":"pith_short_8","alias_value":"IIDCKZDL","created_at":"2026-07-05T01:02:01.488381+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.02148","citing_title":"Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU","json":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU.json","graph_json":"https://pith.science/api/pith-number/IIDCKZDL4IDK2SJQQNGUUMSGIU/graph.json","events_json":"https://pith.science/api/pith-number/IIDCKZDL4IDK2SJQQNGUUMSGIU/events.json","paper":"https://pith.science/paper/IIDCKZDL"},"agent_actions":{"view_html":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU","download_json":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU.json","view_paper":"https://pith.science/paper/IIDCKZDL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.11750&json=true","fetch_graph":"https://pith.science/api/pith-number/IIDCKZDL4IDK2SJQQNGUUMSGIU/graph.json","fetch_events":"https://pith.science/api/pith-number/IIDCKZDL4IDK2SJQQNGUUMSGIU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU/action/storage_attestation","attest_author":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU/action/author_attestation","sign_citation":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU/action/citation_signature","submit_replication":"https://pith.science/pith/IIDCKZDL4IDK2SJQQNGUUMSGIU/action/replication_record"}},"created_at":"2026-07-05T01:02:01.488381+00:00","updated_at":"2026-07-05T01:02:01.488381+00:00"}