{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:MEQTT24E333RQBUPFASCVUVENV","short_pith_number":"pith:MEQTT24E","schema_version":"1.0","canonical_sha256":"612139eb84def718068f28242ad2a46d563e2370db90fae54fa6f70c367d2e52","source":{"kind":"arxiv","id":"2111.00383","version":2},"attestation_state":"computed","paper":{"title":"Relevant Region Sampling Strategy with Adaptive Heuristic for Asymptotically Optimal Path Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chenming Li, Fei Meng, Han Ma, Jiankun Wang, Max Q.-H. Meng","submitted_at":"2021-10-31T02:35:19Z","abstract_excerpt":"Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the most promising regions, which significantly reduces planning time-consumption. The RRT# algorithm defines the Relevant Region based on the cost-to-come provided by the optimal forward-searching tree. However, it uses the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. To improve the path planning efficiency, we propose a batch sampling method that samples in a r"},"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":"2111.00383","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-10-31T02:35:19Z","cross_cats_sorted":[],"title_canon_sha256":"b0d6b9ab1bd5e5350fbfa02280778ef3a9ec2de0a9789175bc95151a26d94a58","abstract_canon_sha256":"e5d986aec4aeae7d72da0cbbcd44c18eee1e2aaf052ce91a12c43d8059334764"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:35.887635Z","signature_b64":"26zFKyXS81iu8rTPaX788RaxX+9Wk6YGhCdrCxSJywo3UgK1UJNgjXDKggl9EnRPEwNmxeJ1yUdlOiZWx1inBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"612139eb84def718068f28242ad2a46d563e2370db90fae54fa6f70c367d2e52","last_reissued_at":"2026-07-05T06:13:35.887221Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:35.887221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Relevant Region Sampling Strategy with Adaptive Heuristic for Asymptotically Optimal Path Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chenming Li, Fei Meng, Han Ma, Jiankun Wang, Max Q.-H. Meng","submitted_at":"2021-10-31T02:35:19Z","abstract_excerpt":"Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the most promising regions, which significantly reduces planning time-consumption. The RRT# algorithm defines the Relevant Region based on the cost-to-come provided by the optimal forward-searching tree. However, it uses the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. To improve the path planning efficiency, we propose a batch sampling method that samples in a r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.00383","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/2111.00383/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":"2111.00383","created_at":"2026-07-05T06:13:35.887278+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.00383v2","created_at":"2026-07-05T06:13:35.887278+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.00383","created_at":"2026-07-05T06:13:35.887278+00:00"},{"alias_kind":"pith_short_12","alias_value":"MEQTT24E333R","created_at":"2026-07-05T06:13:35.887278+00:00"},{"alias_kind":"pith_short_16","alias_value":"MEQTT24E333RQBUP","created_at":"2026-07-05T06:13:35.887278+00:00"},{"alias_kind":"pith_short_8","alias_value":"MEQTT24E","created_at":"2026-07-05T06:13:35.887278+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/MEQTT24E333RQBUPFASCVUVENV","json":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV.json","graph_json":"https://pith.science/api/pith-number/MEQTT24E333RQBUPFASCVUVENV/graph.json","events_json":"https://pith.science/api/pith-number/MEQTT24E333RQBUPFASCVUVENV/events.json","paper":"https://pith.science/paper/MEQTT24E"},"agent_actions":{"view_html":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV","download_json":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV.json","view_paper":"https://pith.science/paper/MEQTT24E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.00383&json=true","fetch_graph":"https://pith.science/api/pith-number/MEQTT24E333RQBUPFASCVUVENV/graph.json","fetch_events":"https://pith.science/api/pith-number/MEQTT24E333RQBUPFASCVUVENV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV/action/storage_attestation","attest_author":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV/action/author_attestation","sign_citation":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV/action/citation_signature","submit_replication":"https://pith.science/pith/MEQTT24E333RQBUPFASCVUVENV/action/replication_record"}},"created_at":"2026-07-05T06:13:35.887278+00:00","updated_at":"2026-07-05T06:13:35.887278+00:00"}