{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CRG6ZS4SB4VUNG4PW5E7VQDYF7","short_pith_number":"pith:CRG6ZS4S","schema_version":"1.0","canonical_sha256":"144deccb920f2b469b8fb749fac0782ff0813a5fc5ea97bf4c6a1408c6b4829c","source":{"kind":"arxiv","id":"2501.14526","version":1},"attestation_state":"computed","paper":{"title":"Robustified Time-optimal Point-to-point Motion Planning and Control under Uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Jan Swevers, Shuhao Zhang","submitted_at":"2025-01-24T14:29:58Z","abstract_excerpt":"This paper proposes a novel approach to formulate time-optimal point-to-point motion planning and control under uncertainty. The approach defines a robustified two-stage Optimal Control Problem (OCP), in which stage 1, with a fixed time grid, is seamlessly stitched with stage 2, which features a variable time grid. Stage 1 optimizes not only the nominal trajectory, but also feedback gains and corresponding state covariances, which robustify constraints in both stages. The outcome is a minimized uncertainty in stage 1 and a minimized total motion time for stage 2, both contributing to the time "},"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":"2501.14526","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-24T14:29:58Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"1b2a6e6f2957852fc21e0d9352ea1a4b2538b53c966820532bed904431eaabe0","abstract_canon_sha256":"3ce6cb1d675a21412efd6c1d1cd2599ecbfc03a8d15e44cf7f94b23ac5223fbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:02.228171Z","signature_b64":"5nylfj1SWwwTmP1HTbFuZhfK6eaE+hawZtpOY2CQ/nQSLLtFBVYEwrBshpBGeLaaKqSiEVPL9XCzaRTHXR7RCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"144deccb920f2b469b8fb749fac0782ff0813a5fc5ea97bf4c6a1408c6b4829c","last_reissued_at":"2026-07-05T10:05:02.227698Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:02.227698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robustified Time-optimal Point-to-point Motion Planning and Control under Uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Jan Swevers, Shuhao Zhang","submitted_at":"2025-01-24T14:29:58Z","abstract_excerpt":"This paper proposes a novel approach to formulate time-optimal point-to-point motion planning and control under uncertainty. The approach defines a robustified two-stage Optimal Control Problem (OCP), in which stage 1, with a fixed time grid, is seamlessly stitched with stage 2, which features a variable time grid. Stage 1 optimizes not only the nominal trajectory, but also feedback gains and corresponding state covariances, which robustify constraints in both stages. The outcome is a minimized uncertainty in stage 1 and a minimized total motion time for stage 2, both contributing to the time "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14526","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/2501.14526/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":"2501.14526","created_at":"2026-07-05T10:05:02.227756+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.14526v1","created_at":"2026-07-05T10:05:02.227756+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14526","created_at":"2026-07-05T10:05:02.227756+00:00"},{"alias_kind":"pith_short_12","alias_value":"CRG6ZS4SB4VU","created_at":"2026-07-05T10:05:02.227756+00:00"},{"alias_kind":"pith_short_16","alias_value":"CRG6ZS4SB4VUNG4P","created_at":"2026-07-05T10:05:02.227756+00:00"},{"alias_kind":"pith_short_8","alias_value":"CRG6ZS4S","created_at":"2026-07-05T10:05:02.227756+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.13622","citing_title":"Disturbance-aware minimum-time planning strategies for motorsport vehicles with probabilistic safety certificates","ref_index":13,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7","json":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7.json","graph_json":"https://pith.science/api/pith-number/CRG6ZS4SB4VUNG4PW5E7VQDYF7/graph.json","events_json":"https://pith.science/api/pith-number/CRG6ZS4SB4VUNG4PW5E7VQDYF7/events.json","paper":"https://pith.science/paper/CRG6ZS4S"},"agent_actions":{"view_html":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7","download_json":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7.json","view_paper":"https://pith.science/paper/CRG6ZS4S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.14526&json=true","fetch_graph":"https://pith.science/api/pith-number/CRG6ZS4SB4VUNG4PW5E7VQDYF7/graph.json","fetch_events":"https://pith.science/api/pith-number/CRG6ZS4SB4VUNG4PW5E7VQDYF7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7/action/storage_attestation","attest_author":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7/action/author_attestation","sign_citation":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7/action/citation_signature","submit_replication":"https://pith.science/pith/CRG6ZS4SB4VUNG4PW5E7VQDYF7/action/replication_record"}},"created_at":"2026-07-05T10:05:02.227756+00:00","updated_at":"2026-07-05T10:05:02.227756+00:00"}