{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AP6KLWSZ5TRKTOKEGGAFGUCXCG","short_pith_number":"pith:AP6KLWSZ","schema_version":"1.0","canonical_sha256":"03fca5da59ece2a9b944318053505711a597bc3760c670fc349ed89d37deea2d","source":{"kind":"arxiv","id":"2606.31197","version":1},"attestation_state":"computed","paper":{"title":"Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Haoang Li, Hongliang Lu, Tianshun Li, Xinhu Zheng","submitted_at":"2026-06-30T06:26:34Z","abstract_excerpt":"Accurate 4D trajectory prediction and closed-loop tracking are essential for Unmanned Aerial Vehicle (UAV) swarms to achieve safe and efficient operations in complex low-altitude environments such as urban airspaces, industrial sites, and indoor facilities. However, this task remains challenging due to intrinsic nonlinearity of UAV swarm dynamics and strict real-time constraints of swarm formation control. To address these challenges, we propose a unified framework that couples coarse-to-fine trajectory forecasting with uncertainty-aware Distributed Nonlinear Model Predictive Control (DNMPC). "},"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":"2606.31197","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-30T06:26:34Z","cross_cats_sorted":[],"title_canon_sha256":"0814d5d0ab1bf81fab32ff679a6f22da5559eff321285e5a920bea252dd00f7a","abstract_canon_sha256":"a893d03dc2ae23227f1d95b51cbc01114992121ab7d88556950ca916336e986b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:17:31.921141Z","signature_b64":"WZj3MPZF4wQMqRRVvlY7Q7swPHZOVd1l4MuEPKKllb9QwcVNMlqJjNpnu0pVc3WFdD/XHeYXXF2u0doauzABDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03fca5da59ece2a9b944318053505711a597bc3760c670fc349ed89d37deea2d","last_reissued_at":"2026-07-01T01:17:31.920748Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:17:31.920748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Haoang Li, Hongliang Lu, Tianshun Li, Xinhu Zheng","submitted_at":"2026-06-30T06:26:34Z","abstract_excerpt":"Accurate 4D trajectory prediction and closed-loop tracking are essential for Unmanned Aerial Vehicle (UAV) swarms to achieve safe and efficient operations in complex low-altitude environments such as urban airspaces, industrial sites, and indoor facilities. However, this task remains challenging due to intrinsic nonlinearity of UAV swarm dynamics and strict real-time constraints of swarm formation control. To address these challenges, we propose a unified framework that couples coarse-to-fine trajectory forecasting with uncertainty-aware Distributed Nonlinear Model Predictive Control (DNMPC). "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31197","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/2606.31197/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":"2606.31197","created_at":"2026-07-01T01:17:31.920802+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.31197v1","created_at":"2026-07-01T01:17:31.920802+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31197","created_at":"2026-07-01T01:17:31.920802+00:00"},{"alias_kind":"pith_short_12","alias_value":"AP6KLWSZ5TRK","created_at":"2026-07-01T01:17:31.920802+00:00"},{"alias_kind":"pith_short_16","alias_value":"AP6KLWSZ5TRKTOKE","created_at":"2026-07-01T01:17:31.920802+00:00"},{"alias_kind":"pith_short_8","alias_value":"AP6KLWSZ","created_at":"2026-07-01T01:17:31.920802+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/AP6KLWSZ5TRKTOKEGGAFGUCXCG","json":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG.json","graph_json":"https://pith.science/api/pith-number/AP6KLWSZ5TRKTOKEGGAFGUCXCG/graph.json","events_json":"https://pith.science/api/pith-number/AP6KLWSZ5TRKTOKEGGAFGUCXCG/events.json","paper":"https://pith.science/paper/AP6KLWSZ"},"agent_actions":{"view_html":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG","download_json":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG.json","view_paper":"https://pith.science/paper/AP6KLWSZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.31197&json=true","fetch_graph":"https://pith.science/api/pith-number/AP6KLWSZ5TRKTOKEGGAFGUCXCG/graph.json","fetch_events":"https://pith.science/api/pith-number/AP6KLWSZ5TRKTOKEGGAFGUCXCG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG/action/storage_attestation","attest_author":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG/action/author_attestation","sign_citation":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG/action/citation_signature","submit_replication":"https://pith.science/pith/AP6KLWSZ5TRKTOKEGGAFGUCXCG/action/replication_record"}},"created_at":"2026-07-01T01:17:31.920802+00:00","updated_at":"2026-07-01T01:17:31.920802+00:00"}