{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:G6DYHAVHJPQO2W2LVJ5SQ7SQLH","short_pith_number":"pith:G6DYHAVH","schema_version":"1.0","canonical_sha256":"37878382a74be0ed5b4baa7b287e5059c6ea24fb6e1a71a0d6b7db00d5502571","source":{"kind":"arxiv","id":"2309.10665","version":1},"attestation_state":"computed","paper":{"title":"Fast-dRRT*: Efficient Multi-Robot Motion Planning for Automated Industrial Manufacturing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andreas Orthey, Andrey Solano, Arne Sieverling, Robert Gieselmann","submitted_at":"2023-09-19T14:48:21Z","abstract_excerpt":"We present Fast-dRRT*, a sampling-based multi-robot planner, for real-time industrial automation scenarios. Fast-dRRT* builds upon the discrete rapidly-exploring random tree (dRRT*) planner, and extends dRRT* by using pre-computed swept volumes for efficient collision detection, deadlock avoidance for partial multi-robot problems, and a simplified rewiring strategy. We evaluate Fast-dRRT* on five challenging multi-robot scenarios using two to four industrial robot arms from various manufacturers. The scenarios comprise situations involving deadlocks, narrow passages, and close proximity tasks."},"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":"2309.10665","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-09-19T14:48:21Z","cross_cats_sorted":[],"title_canon_sha256":"2a1b085f253ab1a0e8081212bb8729f2c260b8ee2cd0ef2220e0d10241eb8d3d","abstract_canon_sha256":"7d2c2e5164e715711e8d6bbc4c4ff7fea70d3663be9a2e89dee974a5724ea7eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:52:15.304672Z","signature_b64":"m/MOgqB3Ens6iW6YU6w9pAjPxdt8OveJbe7vxOjcpDe8vh7+LxNR8yLQuAlV33C6TGOR5s+cOjK8VN/O95MFDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37878382a74be0ed5b4baa7b287e5059c6ea24fb6e1a71a0d6b7db00d5502571","last_reissued_at":"2026-07-05T06:52:15.304169Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:52:15.304169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast-dRRT*: Efficient Multi-Robot Motion Planning for Automated Industrial Manufacturing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andreas Orthey, Andrey Solano, Arne Sieverling, Robert Gieselmann","submitted_at":"2023-09-19T14:48:21Z","abstract_excerpt":"We present Fast-dRRT*, a sampling-based multi-robot planner, for real-time industrial automation scenarios. Fast-dRRT* builds upon the discrete rapidly-exploring random tree (dRRT*) planner, and extends dRRT* by using pre-computed swept volumes for efficient collision detection, deadlock avoidance for partial multi-robot problems, and a simplified rewiring strategy. We evaluate Fast-dRRT* on five challenging multi-robot scenarios using two to four industrial robot arms from various manufacturers. The scenarios comprise situations involving deadlocks, narrow passages, and close proximity tasks."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10665","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/2309.10665/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":"2309.10665","created_at":"2026-07-05T06:52:15.304230+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.10665v1","created_at":"2026-07-05T06:52:15.304230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10665","created_at":"2026-07-05T06:52:15.304230+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6DYHAVHJPQO","created_at":"2026-07-05T06:52:15.304230+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6DYHAVHJPQO2W2L","created_at":"2026-07-05T06:52:15.304230+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6DYHAVH","created_at":"2026-07-05T06:52:15.304230+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12070","citing_title":"Fibration Trees: A Unified Approach to Multi-Robot Motion Planning","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2503.03509","citing_title":"Sampling-Based Multi-Modal Multi-Robot Multi-Goal Path Planning","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH","json":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH.json","graph_json":"https://pith.science/api/pith-number/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/graph.json","events_json":"https://pith.science/api/pith-number/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/events.json","paper":"https://pith.science/paper/G6DYHAVH"},"agent_actions":{"view_html":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH","download_json":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH.json","view_paper":"https://pith.science/paper/G6DYHAVH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.10665&json=true","fetch_graph":"https://pith.science/api/pith-number/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/graph.json","fetch_events":"https://pith.science/api/pith-number/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/action/storage_attestation","attest_author":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/action/author_attestation","sign_citation":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/action/citation_signature","submit_replication":"https://pith.science/pith/G6DYHAVHJPQO2W2LVJ5SQ7SQLH/action/replication_record"}},"created_at":"2026-07-05T06:52:15.304230+00:00","updated_at":"2026-07-05T06:52:15.304230+00:00"}