{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LRWHEZXSWQ3CJSJVNQOVDC4KSO","short_pith_number":"pith:LRWHEZXS","schema_version":"1.0","canonical_sha256":"5c6c7266f2b43624c9356c1d518b8a939f59e9cdd8431eb149ba753ae66d3504","source":{"kind":"arxiv","id":"1905.04152","version":1},"attestation_state":"computed","paper":{"title":"Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NI","stat.ML"],"primary_cat":"cs.SY","authors_text":"Hamid Shiri, Jihong Park, Mehdi Bennis","submitted_at":"2019-05-10T13:07:00Z","abstract_excerpt":"This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting control task, which incurs huge communication energy for exchanging UAV states in real time. We tackle this problem by exploiting a mean-field game (MFG) theoretic control method that requires the UAV state exchanges only once at the initial source. Afterwards, each UAV can contro"},"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":"1905.04152","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2019-05-10T13:07:00Z","cross_cats_sorted":["cs.LG","cs.NI","stat.ML"],"title_canon_sha256":"9d370521c2b8e38aa21db321ce34ddcd792b9152cff8452ce5df758c3870b92c","abstract_canon_sha256":"f9a2d7755f49d857f53a5be9c8e23911a1c0fcd4a707e536e48576ae7a26a7b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:25.438964Z","signature_b64":"hbjynKy/igxxS9SFngE2qtXJF6+1kFdq3X0DlhD5C7tLeOmcBWqzMu16KX3FpM9TLBD/+mWJg23+ZWPcYc19Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c6c7266f2b43624c9356c1d518b8a939f59e9cdd8431eb149ba753ae66d3504","last_reissued_at":"2026-05-17T23:46:25.438432Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:25.438432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NI","stat.ML"],"primary_cat":"cs.SY","authors_text":"Hamid Shiri, Jihong Park, Mehdi Bennis","submitted_at":"2019-05-10T13:07:00Z","abstract_excerpt":"This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting control task, which incurs huge communication energy for exchanging UAV states in real time. We tackle this problem by exploiting a mean-field game (MFG) theoretic control method that requires the UAV state exchanges only once at the initial source. Afterwards, each UAV can contro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04152","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":"1905.04152","created_at":"2026-05-17T23:46:25.438506+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.04152v1","created_at":"2026-05-17T23:46:25.438506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04152","created_at":"2026-05-17T23:46:25.438506+00:00"},{"alias_kind":"pith_short_12","alias_value":"LRWHEZXSWQ3C","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"LRWHEZXSWQ3CJSJV","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"LRWHEZXS","created_at":"2026-05-18T12:33:21.387695+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/LRWHEZXSWQ3CJSJVNQOVDC4KSO","json":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO.json","graph_json":"https://pith.science/api/pith-number/LRWHEZXSWQ3CJSJVNQOVDC4KSO/graph.json","events_json":"https://pith.science/api/pith-number/LRWHEZXSWQ3CJSJVNQOVDC4KSO/events.json","paper":"https://pith.science/paper/LRWHEZXS"},"agent_actions":{"view_html":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO","download_json":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO.json","view_paper":"https://pith.science/paper/LRWHEZXS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.04152&json=true","fetch_graph":"https://pith.science/api/pith-number/LRWHEZXSWQ3CJSJVNQOVDC4KSO/graph.json","fetch_events":"https://pith.science/api/pith-number/LRWHEZXSWQ3CJSJVNQOVDC4KSO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO/action/storage_attestation","attest_author":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO/action/author_attestation","sign_citation":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO/action/citation_signature","submit_replication":"https://pith.science/pith/LRWHEZXSWQ3CJSJVNQOVDC4KSO/action/replication_record"}},"created_at":"2026-05-17T23:46:25.438506+00:00","updated_at":"2026-05-17T23:46:25.438506+00:00"}