{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:G3MEEECNTZBJG5FPBYLJ5FIGEP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2d2f7d4d147ef2353900285c7f6d298b94787d02c5a4b13ee3c832d801618a27","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-06-05T08:38:52Z","title_canon_sha256":"97444fbbe512fa1a867340f28594d85cd96d9c011694a6d47b69ef6efabc63cc"},"schema_version":"1.0","source":{"id":"2506.04752","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.04752","created_at":"2026-07-05T11:16:36Z"},{"alias_kind":"arxiv_version","alias_value":"2506.04752v1","created_at":"2026-07-05T11:16:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04752","created_at":"2026-07-05T11:16:36Z"},{"alias_kind":"pith_short_12","alias_value":"G3MEEECNTZBJ","created_at":"2026-07-05T11:16:36Z"},{"alias_kind":"pith_short_16","alias_value":"G3MEEECNTZBJG5FP","created_at":"2026-07-05T11:16:36Z"},{"alias_kind":"pith_short_8","alias_value":"G3MEEECN","created_at":"2026-07-05T11:16:36Z"}],"graph_snapshots":[{"event_id":"sha256:26b6137040d1d2e7bc4249709de326846448b8dd8723af8ecc47b0b01cad0243","target":"graph","created_at":"2026-07-05T11:16:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.04752/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-axle Swerve-drive Autonomous Mobile Robots (MS-AGVs) equipped with independently steerable wheels are commonly used for high-payload transportation. In this work, we present a novel model predictive control (MPC) method for MS-AGV trajectory tracking that takes tire wear minimization consideration in the objective function. To speed up the problem-solving process, we propose a hierarchical controller design and simplify the dynamic model by integrating the \\textit{magic formula tire model} and \\textit{simplified tire wear model}. In the experiment, the proposed method can be solved by si","authors_text":"Fen Liu, Lihua Xie, Shenghai Yuan, Thien-Minh Nguyen, Tianxin Hu, Xinhang Xu","cross_cats":["math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-06-05T08:38:52Z","title":"Tire Wear Aware Trajectory Tracking Control for Multi-axle Swerve-drive Autonomous Mobile Robots"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04752","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dd8d3bffab306468a98d00e32f94f64c477e4ce7fbdae9fef2a47210889fd4bf","target":"record","created_at":"2026-07-05T11:16:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2d2f7d4d147ef2353900285c7f6d298b94787d02c5a4b13ee3c832d801618a27","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-06-05T08:38:52Z","title_canon_sha256":"97444fbbe512fa1a867340f28594d85cd96d9c011694a6d47b69ef6efabc63cc"},"schema_version":"1.0","source":{"id":"2506.04752","kind":"arxiv","version":1}},"canonical_sha256":"36d842104d9e429374af0e169e950623c7e2c628bcb37ff2f0f1b935d2307e6a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"36d842104d9e429374af0e169e950623c7e2c628bcb37ff2f0f1b935d2307e6a","first_computed_at":"2026-07-05T11:16:36.749952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:16:36.749952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v9uU0srLe0ER/lHKND2V9JFG0G4WiEzd2cjktLzGLbLdsymm4eIbX85kSfEs68eI+NdJ8e0+CLkhZ2dO3BoyAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:16:36.750631Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.04752","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd8d3bffab306468a98d00e32f94f64c477e4ce7fbdae9fef2a47210889fd4bf","sha256:26b6137040d1d2e7bc4249709de326846448b8dd8723af8ecc47b0b01cad0243"],"state_sha256":"f712d19e97b0693f355ffbe29d713b888450969f92123911536a0db4a0217f8a"}