{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PP7UU2OVC2IYESRQMJX6DEXECA","short_pith_number":"pith:PP7UU2OV","schema_version":"1.0","canonical_sha256":"7bff4a69d51691824a30626fe192e410279042a498f547049e0e903c20f309e7","source":{"kind":"arxiv","id":"1908.08493","version":1},"attestation_state":"computed","paper":{"title":"A Hybrid Method for Online Trajectory Planning of Mobile Robots in Cluttered Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"David G\\'omez-Guti\\'errez, Jos\\'e I. Parra-Vilchis, Leobardo Campos-Mac\\'ias, Rafael de la Guardia, Rodrigo Aldana-L\\'opez","submitted_at":"2019-08-22T16:58:06Z","abstract_excerpt":"This paper presents a method for online trajectory planning in known environments. The proposed algorithm is a fusion of sampling-based techniques and model-based optimization via quadratic programming. The former is used to efficiently generate an obstacle-free path while the latter takes into account the robot dynamical constraints to generate a time-dependent trajectory. The main contribution of this work lies on the formulation of a convex optimization problem over the generated obstacle-free path that is guaranteed to be feasible. Thus, in contrast with previously proposed methods, iterat"},"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":"1908.08493","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-08-22T16:58:06Z","cross_cats_sorted":[],"title_canon_sha256":"ce1d1bd1d1edf275429260a96757dcc59a307441e2c6406c588835b2f0d112b4","abstract_canon_sha256":"92d6a6306944ac03b9a6a4d700ebd5795afd715eb8d08d99dce960bea6df48d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:13.385828Z","signature_b64":"ZujUi9+gx8yTcPbUtMqknUxC48erLovStTQKdHMiDoC+gcaMgodU5P3PklEGWjzfATr9waWbc8weYPPFDvAhBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bff4a69d51691824a30626fe192e410279042a498f547049e0e903c20f309e7","last_reissued_at":"2026-07-04T23:59:13.385407Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:13.385407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Hybrid Method for Online Trajectory Planning of Mobile Robots in Cluttered Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"David G\\'omez-Guti\\'errez, Jos\\'e I. Parra-Vilchis, Leobardo Campos-Mac\\'ias, Rafael de la Guardia, Rodrigo Aldana-L\\'opez","submitted_at":"2019-08-22T16:58:06Z","abstract_excerpt":"This paper presents a method for online trajectory planning in known environments. The proposed algorithm is a fusion of sampling-based techniques and model-based optimization via quadratic programming. The former is used to efficiently generate an obstacle-free path while the latter takes into account the robot dynamical constraints to generate a time-dependent trajectory. The main contribution of this work lies on the formulation of a convex optimization problem over the generated obstacle-free path that is guaranteed to be feasible. Thus, in contrast with previously proposed methods, iterat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08493","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/1908.08493/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":"1908.08493","created_at":"2026-07-04T23:59:13.385464+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.08493v1","created_at":"2026-07-04T23:59:13.385464+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08493","created_at":"2026-07-04T23:59:13.385464+00:00"},{"alias_kind":"pith_short_12","alias_value":"PP7UU2OVC2IY","created_at":"2026-07-04T23:59:13.385464+00:00"},{"alias_kind":"pith_short_16","alias_value":"PP7UU2OVC2IYESRQ","created_at":"2026-07-04T23:59:13.385464+00:00"},{"alias_kind":"pith_short_8","alias_value":"PP7UU2OV","created_at":"2026-07-04T23:59:13.385464+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/PP7UU2OVC2IYESRQMJX6DEXECA","json":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA.json","graph_json":"https://pith.science/api/pith-number/PP7UU2OVC2IYESRQMJX6DEXECA/graph.json","events_json":"https://pith.science/api/pith-number/PP7UU2OVC2IYESRQMJX6DEXECA/events.json","paper":"https://pith.science/paper/PP7UU2OV"},"agent_actions":{"view_html":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA","download_json":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA.json","view_paper":"https://pith.science/paper/PP7UU2OV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.08493&json=true","fetch_graph":"https://pith.science/api/pith-number/PP7UU2OVC2IYESRQMJX6DEXECA/graph.json","fetch_events":"https://pith.science/api/pith-number/PP7UU2OVC2IYESRQMJX6DEXECA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA/action/storage_attestation","attest_author":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA/action/author_attestation","sign_citation":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA/action/citation_signature","submit_replication":"https://pith.science/pith/PP7UU2OVC2IYESRQMJX6DEXECA/action/replication_record"}},"created_at":"2026-07-04T23:59:13.385464+00:00","updated_at":"2026-07-04T23:59:13.385464+00:00"}