{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:23SRMDS2E2KKGRZ7RRQXB633LO","short_pith_number":"pith:23SRMDS2","schema_version":"1.0","canonical_sha256":"d6e5160e5a2694a3473f8c6170fb7b5b889ce1133aff5b9592f7bb737e835fb3","source":{"kind":"arxiv","id":"1710.06092","version":1},"attestation_state":"computed","paper":{"title":"Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Cole Gulino, Daqing Yi, Oren Salzman, Rohan Thakker, Siddhartha Srinivasa","submitted_at":"2017-10-17T04:36:57Z","abstract_excerpt":"Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion planning problem lies in a Euclidean space, this region $X_{inf}$, called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling"},"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":"1710.06092","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-10-17T04:36:57Z","cross_cats_sorted":[],"title_canon_sha256":"a0d99bd4ade7750b00d240426bb0d62eeb313c76e7c12b74d2706f39ac6ce003","abstract_canon_sha256":"23eee125a2f327670c751e891a5e08d7b8869fcd17cbda5ff7af4aa9c2785a5d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:37.785975Z","signature_b64":"Itk8qV2ANwG6+j24qJWvRVA3F/yCNHnP7cghbhzc6ccR9/IbPyx4l/8/WdQdjky31rC+P01bqwnthngtOPL3Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6e5160e5a2694a3473f8c6170fb7b5b889ce1133aff5b9592f7bb737e835fb3","last_reissued_at":"2026-05-18T00:32:37.785294Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:37.785294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Cole Gulino, Daqing Yi, Oren Salzman, Rohan Thakker, Siddhartha Srinivasa","submitted_at":"2017-10-17T04:36:57Z","abstract_excerpt":"Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion planning problem lies in a Euclidean space, this region $X_{inf}$, called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06092","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":"1710.06092","created_at":"2026-05-18T00:32:37.785408+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.06092v1","created_at":"2026-05-18T00:32:37.785408+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06092","created_at":"2026-05-18T00:32:37.785408+00:00"},{"alias_kind":"pith_short_12","alias_value":"23SRMDS2E2KK","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"23SRMDS2E2KKGRZ7","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"23SRMDS2","created_at":"2026-05-18T12:30:55.937587+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/23SRMDS2E2KKGRZ7RRQXB633LO","json":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO.json","graph_json":"https://pith.science/api/pith-number/23SRMDS2E2KKGRZ7RRQXB633LO/graph.json","events_json":"https://pith.science/api/pith-number/23SRMDS2E2KKGRZ7RRQXB633LO/events.json","paper":"https://pith.science/paper/23SRMDS2"},"agent_actions":{"view_html":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO","download_json":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO.json","view_paper":"https://pith.science/paper/23SRMDS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.06092&json=true","fetch_graph":"https://pith.science/api/pith-number/23SRMDS2E2KKGRZ7RRQXB633LO/graph.json","fetch_events":"https://pith.science/api/pith-number/23SRMDS2E2KKGRZ7RRQXB633LO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO/action/storage_attestation","attest_author":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO/action/author_attestation","sign_citation":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO/action/citation_signature","submit_replication":"https://pith.science/pith/23SRMDS2E2KKGRZ7RRQXB633LO/action/replication_record"}},"created_at":"2026-05-18T00:32:37.785408+00:00","updated_at":"2026-05-18T00:32:37.785408+00:00"}