{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:O4Q2PZLIN3KVES357EWUISHD5R","short_pith_number":"pith:O4Q2PZLI","canonical_record":{"source":{"id":"1709.00627","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-02T20:28:28Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"b1bb39b9f31922a415fafa31ccb1eff530d60afb274d2a7f6ce9a3ad333a8440","abstract_canon_sha256":"d3283c74ffbf76ac5f5a47917f8c274e35aad875bcb3842ab8f38b9d38dd37a5"},"schema_version":"1.0"},"canonical_sha256":"7721a7e5686ed5524b7df92d4448e3ec5630427ca1a090077a81f7644d57363d","source":{"kind":"arxiv","id":"1709.00627","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.00627","created_at":"2026-05-18T00:15:37Z"},{"alias_kind":"arxiv_version","alias_value":"1709.00627v3","created_at":"2026-05-18T00:15:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00627","created_at":"2026-05-18T00:15:37Z"},{"alias_kind":"pith_short_12","alias_value":"O4Q2PZLIN3KV","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"O4Q2PZLIN3KVES35","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"O4Q2PZLI","created_at":"2026-05-18T12:31:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:O4Q2PZLIN3KVES357EWUISHD5R","target":"record","payload":{"canonical_record":{"source":{"id":"1709.00627","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-02T20:28:28Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"b1bb39b9f31922a415fafa31ccb1eff530d60afb274d2a7f6ce9a3ad333a8440","abstract_canon_sha256":"d3283c74ffbf76ac5f5a47917f8c274e35aad875bcb3842ab8f38b9d38dd37a5"},"schema_version":"1.0"},"canonical_sha256":"7721a7e5686ed5524b7df92d4448e3ec5630427ca1a090077a81f7644d57363d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:37.425698Z","signature_b64":"nDy8v92YRyitMwglF4AxJRaJOOvZSJq8P2VPu/Hz4daM9HInnrKi2q8wO1cgzLJzMthxCzQAlyBFaHZIM9I7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7721a7e5686ed5524b7df92d4448e3ec5630427ca1a090077a81f7644d57363d","last_reissued_at":"2026-05-18T00:15:37.425242Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:37.425242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.00627","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"euNQ1EW6I+SUfq1/9HTLpJ5+h1vs20Xp/pSAzhzKNbsyN1TVdTW8GbijOzu36fzqa6g5sKjqpZcv+3kufrWhCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T04:09:12.227717Z"},"content_sha256":"a2ac410bdf6dc69398697c9e7105ceb0e21aeb22f1e683576554ec9d877b492a","schema_version":"1.0","event_id":"sha256:a2ac410bdf6dc69398697c9e7105ceb0e21aeb22f1e683576554ec9d877b492a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:O4Q2PZLIN3KVES357EWUISHD5R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"math.OC","authors_text":"Changliu Liu, Chung-Yen Lin, Masayoshi Tomizuka","submitted_at":"2017-09-02T20:28:28Z","abstract_excerpt":"With the development of robotics, there are growing needs for real time motion planning. However, due to obstacles in the environment, the planning problem is highly non-convex, which makes it difficult to achieve real time computation using existing non-convex optimization algorithms. This paper introduces the convex feasible set algorithm (CFS) which is a fast algorithm for non-convex optimization problems that have convex costs and non-convex constraints. The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex const"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00627","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sj4eJ8U/fTmY3Me9sb1Js/Ip6bSJrZQfG2MucfI4CxaBO9mxqcBCTjL84Sjp8UJLN54OpHuNa4IPcrovOocDBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T04:09:12.228091Z"},"content_sha256":"d80e6e7439c8f605ab2d922e6e902b7b8da8e5c40dd435627db4ca2143b41ff5","schema_version":"1.0","event_id":"sha256:d80e6e7439c8f605ab2d922e6e902b7b8da8e5c40dd435627db4ca2143b41ff5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O4Q2PZLIN3KVES357EWUISHD5R/bundle.json","state_url":"https://pith.science/pith/O4Q2PZLIN3KVES357EWUISHD5R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O4Q2PZLIN3KVES357EWUISHD5R/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-01T04:09:12Z","links":{"resolver":"https://pith.science/pith/O4Q2PZLIN3KVES357EWUISHD5R","bundle":"https://pith.science/pith/O4Q2PZLIN3KVES357EWUISHD5R/bundle.json","state":"https://pith.science/pith/O4Q2PZLIN3KVES357EWUISHD5R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O4Q2PZLIN3KVES357EWUISHD5R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:O4Q2PZLIN3KVES357EWUISHD5R","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":"d3283c74ffbf76ac5f5a47917f8c274e35aad875bcb3842ab8f38b9d38dd37a5","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-02T20:28:28Z","title_canon_sha256":"b1bb39b9f31922a415fafa31ccb1eff530d60afb274d2a7f6ce9a3ad333a8440"},"schema_version":"1.0","source":{"id":"1709.00627","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.00627","created_at":"2026-05-18T00:15:37Z"},{"alias_kind":"arxiv_version","alias_value":"1709.00627v3","created_at":"2026-05-18T00:15:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00627","created_at":"2026-05-18T00:15:37Z"},{"alias_kind":"pith_short_12","alias_value":"O4Q2PZLIN3KV","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"O4Q2PZLIN3KVES35","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"O4Q2PZLI","created_at":"2026-05-18T12:31:34Z"}],"graph_snapshots":[{"event_id":"sha256:d80e6e7439c8f605ab2d922e6e902b7b8da8e5c40dd435627db4ca2143b41ff5","target":"graph","created_at":"2026-05-18T00:15:37Z","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"},"paper":{"abstract_excerpt":"With the development of robotics, there are growing needs for real time motion planning. However, due to obstacles in the environment, the planning problem is highly non-convex, which makes it difficult to achieve real time computation using existing non-convex optimization algorithms. This paper introduces the convex feasible set algorithm (CFS) which is a fast algorithm for non-convex optimization problems that have convex costs and non-convex constraints. The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex const","authors_text":"Changliu Liu, Chung-Yen Lin, Masayoshi Tomizuka","cross_cats":["cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-02T20:28:28Z","title":"The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00627","kind":"arxiv","version":3},"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:a2ac410bdf6dc69398697c9e7105ceb0e21aeb22f1e683576554ec9d877b492a","target":"record","created_at":"2026-05-18T00:15:37Z","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":"d3283c74ffbf76ac5f5a47917f8c274e35aad875bcb3842ab8f38b9d38dd37a5","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-02T20:28:28Z","title_canon_sha256":"b1bb39b9f31922a415fafa31ccb1eff530d60afb274d2a7f6ce9a3ad333a8440"},"schema_version":"1.0","source":{"id":"1709.00627","kind":"arxiv","version":3}},"canonical_sha256":"7721a7e5686ed5524b7df92d4448e3ec5630427ca1a090077a81f7644d57363d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7721a7e5686ed5524b7df92d4448e3ec5630427ca1a090077a81f7644d57363d","first_computed_at":"2026-05-18T00:15:37.425242Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:37.425242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nDy8v92YRyitMwglF4AxJRaJOOvZSJq8P2VPu/Hz4daM9HInnrKi2q8wO1cgzLJzMthxCzQAlyBFaHZIM9I7DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:37.425698Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.00627","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2ac410bdf6dc69398697c9e7105ceb0e21aeb22f1e683576554ec9d877b492a","sha256:d80e6e7439c8f605ab2d922e6e902b7b8da8e5c40dd435627db4ca2143b41ff5"],"state_sha256":"2d893167654816b8a0fb99a4ddbb46ea0085faac1ccfc1a8ac31f319129bd36d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KwjhDun643lhXrEF4sTQAXSh89Zqymnq+s3tK8/Vm/4HvMNbi9GURZI08tf/c1tN9X+OTAaHgM4H/hhr/DQ7Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T04:09:12.230095Z","bundle_sha256":"8f8a105a69bc70b18cde2e139a696da24a7e5c764457fa52cc9fb33b5315ff4b"}}