{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DKVPE76TXP7TBGPZAM4IQNVXTX","short_pith_number":"pith:DKVPE76T","schema_version":"1.0","canonical_sha256":"1aaaf27fd3bbff3099f903388836b79dca82167172b408c378039714a976055c","source":{"kind":"arxiv","id":"1607.01883","version":5},"attestation_state":"computed","paper":{"title":"Sampling-based Incremental Information Gathering with Applications to Robotic Exploration and Environmental Monitoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Gamini Dissanayake, Jaime Valls Miro, Maani Ghaffari Jadidi","submitted_at":"2016-07-07T06:52:15Z","abstract_excerpt":"In this article, we propose a sampling-based motion planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly-exploring information gathering algorithms and benefits from advantages of sampling-based optimal motion planning algorithms. We propose two information functions and their variants for fast and onli"},"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":"1607.01883","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-07-07T06:52:15Z","cross_cats_sorted":[],"title_canon_sha256":"4c60342462d0d87b69d5876b7cab28a2ca43edc4912dd9a247990fd918ab526d","abstract_canon_sha256":"1c34ccff341f28a99c094277d319ca52d074c44dd27a7be3db865188f7e817c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:21.244844Z","signature_b64":"vqVQ3aqK7DS1L8ZTq0kdmMDyCNErXBiVb70X5sZQqAPj0h0YUtwNTVYW5qNYVkr47H3dbUecaZzk2+cApXY5DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1aaaf27fd3bbff3099f903388836b79dca82167172b408c378039714a976055c","last_reissued_at":"2026-05-17T23:45:21.244033Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:21.244033Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sampling-based Incremental Information Gathering with Applications to Robotic Exploration and Environmental Monitoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Gamini Dissanayake, Jaime Valls Miro, Maani Ghaffari Jadidi","submitted_at":"2016-07-07T06:52:15Z","abstract_excerpt":"In this article, we propose a sampling-based motion planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly-exploring information gathering algorithms and benefits from advantages of sampling-based optimal motion planning algorithms. We propose two information functions and their variants for fast and onli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.01883","kind":"arxiv","version":5},"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":"1607.01883","created_at":"2026-05-17T23:45:21.244165+00:00"},{"alias_kind":"arxiv_version","alias_value":"1607.01883v5","created_at":"2026-05-17T23:45:21.244165+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.01883","created_at":"2026-05-17T23:45:21.244165+00:00"},{"alias_kind":"pith_short_12","alias_value":"DKVPE76TXP7T","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DKVPE76TXP7TBGPZ","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DKVPE76T","created_at":"2026-05-18T12:30:12.583610+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/DKVPE76TXP7TBGPZAM4IQNVXTX","json":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX.json","graph_json":"https://pith.science/api/pith-number/DKVPE76TXP7TBGPZAM4IQNVXTX/graph.json","events_json":"https://pith.science/api/pith-number/DKVPE76TXP7TBGPZAM4IQNVXTX/events.json","paper":"https://pith.science/paper/DKVPE76T"},"agent_actions":{"view_html":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX","download_json":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX.json","view_paper":"https://pith.science/paper/DKVPE76T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1607.01883&json=true","fetch_graph":"https://pith.science/api/pith-number/DKVPE76TXP7TBGPZAM4IQNVXTX/graph.json","fetch_events":"https://pith.science/api/pith-number/DKVPE76TXP7TBGPZAM4IQNVXTX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX/action/storage_attestation","attest_author":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX/action/author_attestation","sign_citation":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX/action/citation_signature","submit_replication":"https://pith.science/pith/DKVPE76TXP7TBGPZAM4IQNVXTX/action/replication_record"}},"created_at":"2026-05-17T23:45:21.244165+00:00","updated_at":"2026-05-17T23:45:21.244165+00:00"}