{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RKGP6DTRVFX6P55IXQ2MHIR6SQ","short_pith_number":"pith:RKGP6DTR","schema_version":"1.0","canonical_sha256":"8a8cff0e71a96fe7f7a8bc34c3a23e9410a17c73071b7e491bf5180d5f274ec3","source":{"kind":"arxiv","id":"2402.19033","version":2},"attestation_state":"computed","paper":{"title":"High-Speed Motion Planning for Aerial Swarms in Unknown and Cluttered Environments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Charbel Toumieh, Dario Floreano","submitted_at":"2024-02-29T10:48:53Z","abstract_excerpt":"Coordinated flight of multiple drones allows to achieve tasks faster such as search and rescue and infrastructure inspection. Thus, pushing the state-of-the-art of aerial swarms in navigation speed and robustness is of tremendous benefit. In particular, being able to account for unexplored/unknown environments when planning trajectories allows for safer flight. In this work, we propose the first high-speed, decentralized, and synchronous motion planning framework (HDSM) for an aerial swarm that explicitly takes into account the unknown/undiscovered parts of the environment. The proposed approa"},"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":"2402.19033","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-02-29T10:48:53Z","cross_cats_sorted":[],"title_canon_sha256":"555f9b74052e6cec9e094059b0c1a6465fbcd333bf881f65a8eede60193d7652","abstract_canon_sha256":"d73e51033d443d8e2920cceac10d2618ece2d5232077c22cf4c061f20779d18b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:08.835064Z","signature_b64":"Keq2RCqrfA2Z/9lEFZ9lJhN/PWDrf2LwbzWBF0wasnUbljTnH+KADWX4p6qJSASl8b00epOhHUsw0FsPZDp+DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a8cff0e71a96fe7f7a8bc34c3a23e9410a17c73071b7e491bf5180d5f274ec3","last_reissued_at":"2026-07-05T08:43:08.834641Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:08.834641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High-Speed Motion Planning for Aerial Swarms in Unknown and Cluttered Environments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Charbel Toumieh, Dario Floreano","submitted_at":"2024-02-29T10:48:53Z","abstract_excerpt":"Coordinated flight of multiple drones allows to achieve tasks faster such as search and rescue and infrastructure inspection. Thus, pushing the state-of-the-art of aerial swarms in navigation speed and robustness is of tremendous benefit. In particular, being able to account for unexplored/unknown environments when planning trajectories allows for safer flight. In this work, we propose the first high-speed, decentralized, and synchronous motion planning framework (HDSM) for an aerial swarm that explicitly takes into account the unknown/undiscovered parts of the environment. The proposed approa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.19033","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/2402.19033/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":"2402.19033","created_at":"2026-07-05T08:43:08.834697+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.19033v2","created_at":"2026-07-05T08:43:08.834697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.19033","created_at":"2026-07-05T08:43:08.834697+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKGP6DTRVFX6","created_at":"2026-07-05T08:43:08.834697+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKGP6DTRVFX6P55I","created_at":"2026-07-05T08:43:08.834697+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKGP6DTR","created_at":"2026-07-05T08:43:08.834697+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.16734","citing_title":"DYNUS: Uncertainty-aware Trajectory Planner in Dynamic Unknown Environments","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ","json":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ.json","graph_json":"https://pith.science/api/pith-number/RKGP6DTRVFX6P55IXQ2MHIR6SQ/graph.json","events_json":"https://pith.science/api/pith-number/RKGP6DTRVFX6P55IXQ2MHIR6SQ/events.json","paper":"https://pith.science/paper/RKGP6DTR"},"agent_actions":{"view_html":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ","download_json":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ.json","view_paper":"https://pith.science/paper/RKGP6DTR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.19033&json=true","fetch_graph":"https://pith.science/api/pith-number/RKGP6DTRVFX6P55IXQ2MHIR6SQ/graph.json","fetch_events":"https://pith.science/api/pith-number/RKGP6DTRVFX6P55IXQ2MHIR6SQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ/action/storage_attestation","attest_author":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ/action/author_attestation","sign_citation":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ/action/citation_signature","submit_replication":"https://pith.science/pith/RKGP6DTRVFX6P55IXQ2MHIR6SQ/action/replication_record"}},"created_at":"2026-07-05T08:43:08.834697+00:00","updated_at":"2026-07-05T08:43:08.834697+00:00"}