{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YBT3VASRHVLA7TZS65KMRPVCIY","short_pith_number":"pith:YBT3VASR","schema_version":"1.0","canonical_sha256":"c067ba82513d560fcf32f754c8bea2460308cc13a0b7fddaa428efd099e90eee","source":{"kind":"arxiv","id":"2501.07566","version":1},"attestation_state":"computed","paper":{"title":"SafeSwarm: Decentralized Safe RL for the Swarm of Drones Landing in Dense Crowds","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Aleksey Fedoseev, Demetros Aschu, Dzmitry Tsetserukou, Grik Tadevosyan, Maksim Osipenko, Oleg Sautenkov, Sausar Karaf, Valerii Serpiva","submitted_at":"2025-01-13T18:54:02Z","abstract_excerpt":"This paper introduces a safe swarm of drones capable of performing landings in crowded environments robustly by relying on Reinforcement Learning techniques combined with Safe Learning. The developed system allows us to teach the swarm of drones with different dynamics to land on moving landing pads in an environment while avoiding collisions with obstacles and between agents.\n  The safe barrier net algorithm was developed and evaluated using a swarm of Crazyflie 2.1 micro quadrotors, which were tested indoors with the Vicon motion capture system to ensure precise localization and control.\n  E"},"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":"2501.07566","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-13T18:54:02Z","cross_cats_sorted":[],"title_canon_sha256":"b376fb94b7d94a456f37a9c5f3360c9fa69e20526ff8b72e07844633aa3fa3cd","abstract_canon_sha256":"febcc42b386a2fc099b8298eff5c39d6069b4c701c0ec231a851228a19c8abb3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:24:29.913489Z","signature_b64":"2hFGwnmmKWSwyNbZivFggsNVjGMQwIk1kzq2wGjAMA/sIiGCBzGbFbcnM0kO6XgHAJkgVm5oJNpHsUVTHo3uCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c067ba82513d560fcf32f754c8bea2460308cc13a0b7fddaa428efd099e90eee","last_reissued_at":"2026-07-05T10:24:29.912584Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:24:29.912584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SafeSwarm: Decentralized Safe RL for the Swarm of Drones Landing in Dense Crowds","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Aleksey Fedoseev, Demetros Aschu, Dzmitry Tsetserukou, Grik Tadevosyan, Maksim Osipenko, Oleg Sautenkov, Sausar Karaf, Valerii Serpiva","submitted_at":"2025-01-13T18:54:02Z","abstract_excerpt":"This paper introduces a safe swarm of drones capable of performing landings in crowded environments robustly by relying on Reinforcement Learning techniques combined with Safe Learning. The developed system allows us to teach the swarm of drones with different dynamics to land on moving landing pads in an environment while avoiding collisions with obstacles and between agents.\n  The safe barrier net algorithm was developed and evaluated using a swarm of Crazyflie 2.1 micro quadrotors, which were tested indoors with the Vicon motion capture system to ensure precise localization and control.\n  E"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.07566","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/2501.07566/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":"2501.07566","created_at":"2026-07-05T10:24:29.912686+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.07566v1","created_at":"2026-07-05T10:24:29.912686+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.07566","created_at":"2026-07-05T10:24:29.912686+00:00"},{"alias_kind":"pith_short_12","alias_value":"YBT3VASRHVLA","created_at":"2026-07-05T10:24:29.912686+00:00"},{"alias_kind":"pith_short_16","alias_value":"YBT3VASRHVLA7TZS","created_at":"2026-07-05T10:24:29.912686+00:00"},{"alias_kind":"pith_short_8","alias_value":"YBT3VASR","created_at":"2026-07-05T10:24:29.912686+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/YBT3VASRHVLA7TZS65KMRPVCIY","json":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY.json","graph_json":"https://pith.science/api/pith-number/YBT3VASRHVLA7TZS65KMRPVCIY/graph.json","events_json":"https://pith.science/api/pith-number/YBT3VASRHVLA7TZS65KMRPVCIY/events.json","paper":"https://pith.science/paper/YBT3VASR"},"agent_actions":{"view_html":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY","download_json":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY.json","view_paper":"https://pith.science/paper/YBT3VASR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.07566&json=true","fetch_graph":"https://pith.science/api/pith-number/YBT3VASRHVLA7TZS65KMRPVCIY/graph.json","fetch_events":"https://pith.science/api/pith-number/YBT3VASRHVLA7TZS65KMRPVCIY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY/action/storage_attestation","attest_author":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY/action/author_attestation","sign_citation":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY/action/citation_signature","submit_replication":"https://pith.science/pith/YBT3VASRHVLA7TZS65KMRPVCIY/action/replication_record"}},"created_at":"2026-07-05T10:24:29.912686+00:00","updated_at":"2026-07-05T10:24:29.912686+00:00"}