{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:76XQJLGEXA7UZGAWRHWYOL52QH","short_pith_number":"pith:76XQJLGE","schema_version":"1.0","canonical_sha256":"ffaf04acc4b83f4c981689ed872fba81c486f92bfa93ec5461b055d9bc310472","source":{"kind":"arxiv","id":"2203.01370","version":1},"attestation_state":"computed","paper":{"title":"Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Laura Jarin-Lipschitz, Vijay Kumar, Xu Liu, Yuezhan Tao","submitted_at":"2022-03-02T19:33:33Z","abstract_excerpt":"Fast, autonomous flight in unstructured, cluttered environments such as forests is challenging because it requires the robot to compute new plans in realtime on a computationally-constrained platform. In this paper, we enable this capability with a search-based planning framework that adapts sampling density in realtime to find dynamically-feasible plans while remaining computationally tractable. A paramount challenge in search-based planning is that dense obstacles both necessitate large graphs (to guarantee completeness) and reduce the efficiency of graph search (as heuristics become less ac"},"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":"2203.01370","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-03-02T19:33:33Z","cross_cats_sorted":[],"title_canon_sha256":"514279df800086950c5298eef5e927d0e1107a7e22a2f7a19a1e4cf41b3a362e","abstract_canon_sha256":"bb94abb6e685dde7169cf51f336798b10dfe0a3fb20bbe3f98e2a54ef98ad319"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:01:47.095661Z","signature_b64":"x80M3DLNgsjFjlrJUKVyJDuni5Cz20CB7UIxC9nH1li+wr9w/N1lvFWrBdvKgjJRuFGI/01onJ3R9D+lhlp8Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffaf04acc4b83f4c981689ed872fba81c486f92bfa93ec5461b055d9bc310472","last_reissued_at":"2026-07-05T04:01:47.095074Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:01:47.095074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Laura Jarin-Lipschitz, Vijay Kumar, Xu Liu, Yuezhan Tao","submitted_at":"2022-03-02T19:33:33Z","abstract_excerpt":"Fast, autonomous flight in unstructured, cluttered environments such as forests is challenging because it requires the robot to compute new plans in realtime on a computationally-constrained platform. In this paper, we enable this capability with a search-based planning framework that adapts sampling density in realtime to find dynamically-feasible plans while remaining computationally tractable. A paramount challenge in search-based planning is that dense obstacles both necessitate large graphs (to guarantee completeness) and reduce the efficiency of graph search (as heuristics become less ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.01370","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/2203.01370/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":"2203.01370","created_at":"2026-07-05T04:01:47.095151+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.01370v1","created_at":"2026-07-05T04:01:47.095151+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.01370","created_at":"2026-07-05T04:01:47.095151+00:00"},{"alias_kind":"pith_short_12","alias_value":"76XQJLGEXA7U","created_at":"2026-07-05T04:01:47.095151+00:00"},{"alias_kind":"pith_short_16","alias_value":"76XQJLGEXA7UZGAW","created_at":"2026-07-05T04:01:47.095151+00:00"},{"alias_kind":"pith_short_8","alias_value":"76XQJLGE","created_at":"2026-07-05T04:01:47.095151+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/76XQJLGEXA7UZGAWRHWYOL52QH","json":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH.json","graph_json":"https://pith.science/api/pith-number/76XQJLGEXA7UZGAWRHWYOL52QH/graph.json","events_json":"https://pith.science/api/pith-number/76XQJLGEXA7UZGAWRHWYOL52QH/events.json","paper":"https://pith.science/paper/76XQJLGE"},"agent_actions":{"view_html":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH","download_json":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH.json","view_paper":"https://pith.science/paper/76XQJLGE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.01370&json=true","fetch_graph":"https://pith.science/api/pith-number/76XQJLGEXA7UZGAWRHWYOL52QH/graph.json","fetch_events":"https://pith.science/api/pith-number/76XQJLGEXA7UZGAWRHWYOL52QH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH/action/storage_attestation","attest_author":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH/action/author_attestation","sign_citation":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH/action/citation_signature","submit_replication":"https://pith.science/pith/76XQJLGEXA7UZGAWRHWYOL52QH/action/replication_record"}},"created_at":"2026-07-05T04:01:47.095151+00:00","updated_at":"2026-07-05T04:01:47.095151+00:00"}