{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SPVWKQCDUBPHGCYEWRRGCEQ7SL","short_pith_number":"pith:SPVWKQCD","schema_version":"1.0","canonical_sha256":"93eb654043a05e730b04b46261121f92feea2a444e9588fe31c1ecc00c1bd537","source":{"kind":"arxiv","id":"1608.05786","version":1},"attestation_state":"computed","paper":{"title":"Design of a Trajectory Tracking Controller for a Nanoquadcopter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.SY","authors_text":"Carlos Luis, J\\'er\\^ome Le Ny","submitted_at":"2016-08-20T06:12:41Z","abstract_excerpt":"The primary purpose of this study is to investigate the system modeling of a nanoquadcopter as well as designing position and trajectory control algorithms, with the ultimate goal of testing the system both in simulation and on a real platform.\n  The open source nanoquadcopter platform named Crazyflie 2.0 was chosen for the project. The first phase consisted in the development of a mathematical model that describes the dynamics of the quadcopter. Secondly, a simulation environment was created to design two different control architectures: cascaded PID position tracker and LQT trajectory tracke"},"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":"1608.05786","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2016-08-20T06:12:41Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"b7150d062dcc3b2da103f28619604143c3c3a8c52c77b283dd9ce96ad449e69c","abstract_canon_sha256":"3353958be9383c3c0abc4dc83b056444984caa6a8217f77797e5f73aec4d5cf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:23.199053Z","signature_b64":"Ek948hOzpQGR1ZNHxuRclJD9lMdgoFMKbECGBYz+nnrd8suSq/uoBcLN5JtOOw5FJPLLxDNeZk/YVN7uycCnCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93eb654043a05e730b04b46261121f92feea2a444e9588fe31c1ecc00c1bd537","last_reissued_at":"2026-05-18T01:08:23.194792Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:23.194792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Design of a Trajectory Tracking Controller for a Nanoquadcopter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.SY","authors_text":"Carlos Luis, J\\'er\\^ome Le Ny","submitted_at":"2016-08-20T06:12:41Z","abstract_excerpt":"The primary purpose of this study is to investigate the system modeling of a nanoquadcopter as well as designing position and trajectory control algorithms, with the ultimate goal of testing the system both in simulation and on a real platform.\n  The open source nanoquadcopter platform named Crazyflie 2.0 was chosen for the project. The first phase consisted in the development of a mathematical model that describes the dynamics of the quadcopter. Secondly, a simulation environment was created to design two different control architectures: cascaded PID position tracker and LQT trajectory tracke"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05786","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":"1608.05786","created_at":"2026-05-18T01:08:23.198396+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.05786v1","created_at":"2026-05-18T01:08:23.198396+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.05786","created_at":"2026-05-18T01:08:23.198396+00:00"},{"alias_kind":"pith_short_12","alias_value":"SPVWKQCDUBPH","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SPVWKQCDUBPHGCYE","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SPVWKQCD","created_at":"2026-05-18T12:30:44.179134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2501.18490","citing_title":"Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2504.02710","citing_title":"Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL","json":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL.json","graph_json":"https://pith.science/api/pith-number/SPVWKQCDUBPHGCYEWRRGCEQ7SL/graph.json","events_json":"https://pith.science/api/pith-number/SPVWKQCDUBPHGCYEWRRGCEQ7SL/events.json","paper":"https://pith.science/paper/SPVWKQCD"},"agent_actions":{"view_html":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL","download_json":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL.json","view_paper":"https://pith.science/paper/SPVWKQCD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.05786&json=true","fetch_graph":"https://pith.science/api/pith-number/SPVWKQCDUBPHGCYEWRRGCEQ7SL/graph.json","fetch_events":"https://pith.science/api/pith-number/SPVWKQCDUBPHGCYEWRRGCEQ7SL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL/action/storage_attestation","attest_author":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL/action/author_attestation","sign_citation":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL/action/citation_signature","submit_replication":"https://pith.science/pith/SPVWKQCDUBPHGCYEWRRGCEQ7SL/action/replication_record"}},"created_at":"2026-05-18T01:08:23.198396+00:00","updated_at":"2026-05-18T01:08:23.198396+00:00"}