{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZFIQXNI6IFPWMIG7EUM4S4IHBO","short_pith_number":"pith:ZFIQXNI6","schema_version":"1.0","canonical_sha256":"c9510bb51e415f6620df2519c971070b8f72bf9a81d0432ff9628836144ebf9d","source":{"kind":"arxiv","id":"2506.16386","version":2},"attestation_state":"computed","paper":{"title":"CSC-MPPI: A Novel Constrained MPPI Framework with DBSCAN for Reliable Obstacle Avoidance","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Keunwoo Jang, Leesai Park, Sanghyun Kim","submitted_at":"2025-06-19T15:17:35Z","abstract_excerpt":"This paper proposes Constrained Sampling Cluster Model Predictive Path Integral (CSC-MPPI), a novel constrained formulation of MPPI designed to enhance trajectory optimization while enforcing strict constraints on system states and control inputs. Traditional MPPI, which relies on a probabilistic sampling process, often struggles with constraint satisfaction and generates suboptimal trajectories due to the weighted averaging of sampled trajectories. To address these limitations, the proposed framework integrates a primal-dual gradient-based approach and Density-Based Spatial Clustering of Appl"},"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":"2506.16386","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-06-19T15:17:35Z","cross_cats_sorted":[],"title_canon_sha256":"a8f7897f74ad6f80938d60538f8682317f04da87b2945b7556da1af2738fb77b","abstract_canon_sha256":"b4956b33c0c62fabb964bfcaca0066f025fc8dba33a64506e45033d9fc868f34"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:56.785735Z","signature_b64":"070HjrniiCTn2lgfTeKbcw0kvk5eME+7BdDMC2jG7prNlvAyvqp6XrwvF0OUKk32Ld9ywRktWl0QwcUdTRaMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9510bb51e415f6620df2519c971070b8f72bf9a81d0432ff9628836144ebf9d","last_reissued_at":"2026-07-05T11:35:56.785218Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:56.785218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CSC-MPPI: A Novel Constrained MPPI Framework with DBSCAN for Reliable Obstacle Avoidance","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Keunwoo Jang, Leesai Park, Sanghyun Kim","submitted_at":"2025-06-19T15:17:35Z","abstract_excerpt":"This paper proposes Constrained Sampling Cluster Model Predictive Path Integral (CSC-MPPI), a novel constrained formulation of MPPI designed to enhance trajectory optimization while enforcing strict constraints on system states and control inputs. Traditional MPPI, which relies on a probabilistic sampling process, often struggles with constraint satisfaction and generates suboptimal trajectories due to the weighted averaging of sampled trajectories. To address these limitations, the proposed framework integrates a primal-dual gradient-based approach and Density-Based Spatial Clustering of Appl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16386","kind":"arxiv","version":2},"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/2506.16386/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":"2506.16386","created_at":"2026-07-05T11:35:56.785276+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.16386v2","created_at":"2026-07-05T11:35:56.785276+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16386","created_at":"2026-07-05T11:35:56.785276+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZFIQXNI6IFPW","created_at":"2026-07-05T11:35:56.785276+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZFIQXNI6IFPWMIG7","created_at":"2026-07-05T11:35:56.785276+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZFIQXNI6","created_at":"2026-07-05T11:35:56.785276+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.21364","citing_title":"Multi-Modal Model Predictive Path Integral Control for Collision Avoidance","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO","json":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO.json","graph_json":"https://pith.science/api/pith-number/ZFIQXNI6IFPWMIG7EUM4S4IHBO/graph.json","events_json":"https://pith.science/api/pith-number/ZFIQXNI6IFPWMIG7EUM4S4IHBO/events.json","paper":"https://pith.science/paper/ZFIQXNI6"},"agent_actions":{"view_html":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO","download_json":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO.json","view_paper":"https://pith.science/paper/ZFIQXNI6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.16386&json=true","fetch_graph":"https://pith.science/api/pith-number/ZFIQXNI6IFPWMIG7EUM4S4IHBO/graph.json","fetch_events":"https://pith.science/api/pith-number/ZFIQXNI6IFPWMIG7EUM4S4IHBO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO/action/storage_attestation","attest_author":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO/action/author_attestation","sign_citation":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO/action/citation_signature","submit_replication":"https://pith.science/pith/ZFIQXNI6IFPWMIG7EUM4S4IHBO/action/replication_record"}},"created_at":"2026-07-05T11:35:56.785276+00:00","updated_at":"2026-07-05T11:35:56.785276+00:00"}