{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5N7TLMJ6X5EYZJ2RVIT33BZTNR","short_pith_number":"pith:5N7TLMJ6","schema_version":"1.0","canonical_sha256":"eb7f35b13ebf498ca751aa27bd87336c7e65176f78a16b12064b6404a767a8c8","source":{"kind":"arxiv","id":"1808.00593","version":1},"attestation_state":"computed","paper":{"title":"Perception-driven sparse graphs for optimal motion planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Sertac Karaman, Thomas Sayre-McCord","submitted_at":"2018-08-01T22:59:47Z","abstract_excerpt":"Most existing motion planning algorithms assume that a map (of some quality) is fully determined prior to generating a motion plan. In many emerging applications of robotics, e.g., fast-moving agile aerial robots with constrained embedded computational platforms and visual sensors, dense maps of the world are not immediately available, and they are computationally expensive to construct. We propose a new algorithm for generating plan graphs which couples the perception and motion planning processes for computational efficiency. In a nutshell, the proposed algorithm iteratively switches between"},"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":"1808.00593","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-08-01T22:59:47Z","cross_cats_sorted":[],"title_canon_sha256":"085bb915e8d84bcadb9ed1382f1119e669b2c956a3d96c4292ccbb976aa004d5","abstract_canon_sha256":"a3c9a947ed91f4f835dbdb78af2425a181a3445f07236bbe9e8214c81dcf12b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:04.795984Z","signature_b64":"D6IUKLEkfTY3+FoaYKWxSiR3NQ5GyJcjfckw5AfCWV/e2t6RiaZntdswIarU/ShWnZjSYr4jg0VNisRbNmHhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb7f35b13ebf498ca751aa27bd87336c7e65176f78a16b12064b6404a767a8c8","last_reissued_at":"2026-05-18T00:09:04.795274Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:04.795274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Perception-driven sparse graphs for optimal motion planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Sertac Karaman, Thomas Sayre-McCord","submitted_at":"2018-08-01T22:59:47Z","abstract_excerpt":"Most existing motion planning algorithms assume that a map (of some quality) is fully determined prior to generating a motion plan. In many emerging applications of robotics, e.g., fast-moving agile aerial robots with constrained embedded computational platforms and visual sensors, dense maps of the world are not immediately available, and they are computationally expensive to construct. We propose a new algorithm for generating plan graphs which couples the perception and motion planning processes for computational efficiency. In a nutshell, the proposed algorithm iteratively switches between"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.00593","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":""},"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":"1808.00593","created_at":"2026-05-18T00:09:04.795394+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.00593v1","created_at":"2026-05-18T00:09:04.795394+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.00593","created_at":"2026-05-18T00:09:04.795394+00:00"},{"alias_kind":"pith_short_12","alias_value":"5N7TLMJ6X5EY","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5N7TLMJ6X5EYZJ2R","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5N7TLMJ6","created_at":"2026-05-18T12:32:08.215937+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/5N7TLMJ6X5EYZJ2RVIT33BZTNR","json":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR.json","graph_json":"https://pith.science/api/pith-number/5N7TLMJ6X5EYZJ2RVIT33BZTNR/graph.json","events_json":"https://pith.science/api/pith-number/5N7TLMJ6X5EYZJ2RVIT33BZTNR/events.json","paper":"https://pith.science/paper/5N7TLMJ6"},"agent_actions":{"view_html":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR","download_json":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR.json","view_paper":"https://pith.science/paper/5N7TLMJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.00593&json=true","fetch_graph":"https://pith.science/api/pith-number/5N7TLMJ6X5EYZJ2RVIT33BZTNR/graph.json","fetch_events":"https://pith.science/api/pith-number/5N7TLMJ6X5EYZJ2RVIT33BZTNR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR/action/storage_attestation","attest_author":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR/action/author_attestation","sign_citation":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR/action/citation_signature","submit_replication":"https://pith.science/pith/5N7TLMJ6X5EYZJ2RVIT33BZTNR/action/replication_record"}},"created_at":"2026-05-18T00:09:04.795394+00:00","updated_at":"2026-05-18T00:09:04.795394+00:00"}