{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DJIRZVOXPOUDJLU7NSN2OW3FNV","short_pith_number":"pith:DJIRZVOX","schema_version":"1.0","canonical_sha256":"1a511cd5d77ba834ae9f6c9ba75b656d6b55501985e40a807f4b1acc46ea50cf","source":{"kind":"arxiv","id":"2308.11088","version":1},"attestation_state":"computed","paper":{"title":"Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Bin Guo, Chunyu Tu, Lei Han, Liang Wang, Weihua Shan, Zhiwen Yu, Zhiyong Yu","submitted_at":"2023-08-21T23:54:59Z","abstract_excerpt":"Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as data collection, in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the task completion rate. We propose MANF-RL-RP, a heterogeneous multi-agent route planning algorithm that incorporates several efficient designs, including global-local dual information "},"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":"2308.11088","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-08-21T23:54:59Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"f62b86966407eddb9924b3fed91254a0fee7c2e21c5d5c0168d64286521606bd","abstract_canon_sha256":"999caf758187448b25cf596a55ba9bc4abf46aabf4e846a269966ef8a743c38f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:43:29.481109Z","signature_b64":"RUvoTDss2qJbxjnim3HR9I2DiLq9d4KpYfZD6rTdi4zVIJTO9CvhYgkp2dIkAR92BBNQdOSDoTpxDJInkxTtBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a511cd5d77ba834ae9f6c9ba75b656d6b55501985e40a807f4b1acc46ea50cf","last_reissued_at":"2026-07-05T06:43:29.480685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:43:29.480685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Bin Guo, Chunyu Tu, Lei Han, Liang Wang, Weihua Shan, Zhiwen Yu, Zhiyong Yu","submitted_at":"2023-08-21T23:54:59Z","abstract_excerpt":"Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as data collection, in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the task completion rate. We propose MANF-RL-RP, a heterogeneous multi-agent route planning algorithm that incorporates several efficient designs, including global-local dual information "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.11088","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/2308.11088/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":"2308.11088","created_at":"2026-07-05T06:43:29.480743+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.11088v1","created_at":"2026-07-05T06:43:29.480743+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.11088","created_at":"2026-07-05T06:43:29.480743+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJIRZVOXPOUD","created_at":"2026-07-05T06:43:29.480743+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJIRZVOXPOUDJLU7","created_at":"2026-07-05T06:43:29.480743+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJIRZVOX","created_at":"2026-07-05T06:43:29.480743+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/DJIRZVOXPOUDJLU7NSN2OW3FNV","json":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV.json","graph_json":"https://pith.science/api/pith-number/DJIRZVOXPOUDJLU7NSN2OW3FNV/graph.json","events_json":"https://pith.science/api/pith-number/DJIRZVOXPOUDJLU7NSN2OW3FNV/events.json","paper":"https://pith.science/paper/DJIRZVOX"},"agent_actions":{"view_html":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV","download_json":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV.json","view_paper":"https://pith.science/paper/DJIRZVOX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.11088&json=true","fetch_graph":"https://pith.science/api/pith-number/DJIRZVOXPOUDJLU7NSN2OW3FNV/graph.json","fetch_events":"https://pith.science/api/pith-number/DJIRZVOXPOUDJLU7NSN2OW3FNV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV/action/storage_attestation","attest_author":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV/action/author_attestation","sign_citation":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV/action/citation_signature","submit_replication":"https://pith.science/pith/DJIRZVOXPOUDJLU7NSN2OW3FNV/action/replication_record"}},"created_at":"2026-07-05T06:43:29.480743+00:00","updated_at":"2026-07-05T06:43:29.480743+00:00"}