{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CRV24JECLIONINLPL4TZQ7P7PR","short_pith_number":"pith:CRV24JEC","schema_version":"1.0","canonical_sha256":"146bae24825a1cd4356f5f27987dff7c7e8232263181ffb9b9f82230c3b219ac","source":{"kind":"arxiv","id":"2409.17379","version":1},"attestation_state":"computed","paper":{"title":"Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.RO","authors_text":"Alessandro Saviolo, Giuseppe Loianno, Guanrui Li, Manohari Goarin","submitted_at":"2024-09-25T21:46:44Z","abstract_excerpt":"Multi-quadrotor systems face significant challenges in decentralized control, particularly with safety and coordination under sensing and communication limitations. State-of-the-art methods leverage Control Barrier Functions (CBFs) to provide safety guarantees but often neglect actuation constraints and limited detection range. To address these gaps, we propose a novel decentralized Nonlinear Model Predictive Control (NMPC) that integrates Exponential CBFs (ECBFs) to enhance safety and optimality in multi-quadrotor systems. We provide both conservative and practical minimum bounds of the range"},"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":"2409.17379","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-09-25T21:46:44Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"164cdd3ec773ba6f5b2243f2825c2d1f23e41a2914263295228f40a5910bcbb8","abstract_canon_sha256":"e561a675177ee9c43fc25f6c70d4167847c296a3b31ed9b7f5d3db2abb49cde2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:52.518985Z","signature_b64":"/pdpquCt5PsDaG4Ur7r09SxVss91HZJzQlN0Lh6pjW7m26u5MqLw7g93JZC0Em89J1pQtJrVvIhhHteAHUBIDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"146bae24825a1cd4356f5f27987dff7c7e8232263181ffb9b9f82230c3b219ac","last_reissued_at":"2026-07-05T09:11:52.518480Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:52.518480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.RO","authors_text":"Alessandro Saviolo, Giuseppe Loianno, Guanrui Li, Manohari Goarin","submitted_at":"2024-09-25T21:46:44Z","abstract_excerpt":"Multi-quadrotor systems face significant challenges in decentralized control, particularly with safety and coordination under sensing and communication limitations. State-of-the-art methods leverage Control Barrier Functions (CBFs) to provide safety guarantees but often neglect actuation constraints and limited detection range. To address these gaps, we propose a novel decentralized Nonlinear Model Predictive Control (NMPC) that integrates Exponential CBFs (ECBFs) to enhance safety and optimality in multi-quadrotor systems. We provide both conservative and practical minimum bounds of the range"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17379","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/2409.17379/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":"2409.17379","created_at":"2026-07-05T09:11:52.518541+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17379v1","created_at":"2026-07-05T09:11:52.518541+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17379","created_at":"2026-07-05T09:11:52.518541+00:00"},{"alias_kind":"pith_short_12","alias_value":"CRV24JECLION","created_at":"2026-07-05T09:11:52.518541+00:00"},{"alias_kind":"pith_short_16","alias_value":"CRV24JECLIONINLP","created_at":"2026-07-05T09:11:52.518541+00:00"},{"alias_kind":"pith_short_8","alias_value":"CRV24JEC","created_at":"2026-07-05T09:11:52.518541+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01038","citing_title":"Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR","json":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR.json","graph_json":"https://pith.science/api/pith-number/CRV24JECLIONINLPL4TZQ7P7PR/graph.json","events_json":"https://pith.science/api/pith-number/CRV24JECLIONINLPL4TZQ7P7PR/events.json","paper":"https://pith.science/paper/CRV24JEC"},"agent_actions":{"view_html":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR","download_json":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR.json","view_paper":"https://pith.science/paper/CRV24JEC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17379&json=true","fetch_graph":"https://pith.science/api/pith-number/CRV24JECLIONINLPL4TZQ7P7PR/graph.json","fetch_events":"https://pith.science/api/pith-number/CRV24JECLIONINLPL4TZQ7P7PR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR/action/storage_attestation","attest_author":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR/action/author_attestation","sign_citation":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR/action/citation_signature","submit_replication":"https://pith.science/pith/CRV24JECLIONINLPL4TZQ7P7PR/action/replication_record"}},"created_at":"2026-07-05T09:11:52.518541+00:00","updated_at":"2026-07-05T09:11:52.518541+00:00"}