{"id":"W4400276545","doi":"10.1109/tmech.2024.3408810","title":"Optimal Motion Planning Under Dynamic Risk Region for Safe Human–Robot Cooperation","year":2024,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Motion (physics); Motion planning; Robot; Risk analysis (engineering); Business; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001996011,0.0002401838,0.0001746371,0.000300338,0.0004299903,0.0001615009,0.00009454055,0.0001949878,0.00009145783],"category_scores_gemma":[0.000004114874,0.000264979,0.0001638833,0.0002647986,0.00001762511,0.000350202,0.000001060211,0.0005833555,0.00009455963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004627739,"about_ca_system_score_gemma":0.00003328752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001288179,"about_ca_topic_score_gemma":0.0000329564,"domain_scores_codex":[0.9988348,0.00005057939,0.0003146091,0.0003228722,0.0001654354,0.0003116599],"domain_scores_gemma":[0.9995437,0.00009055853,0.00003599451,0.0002149076,0.00004424421,0.0000706256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001689509,0.00002199983,0.000001425012,0.0000797758,0.00008080763,0.000002025246,0.000308835,0.9840561,0.003687207,0.001905376,0.0001390424,0.009700546],"study_design_scores_gemma":[0.0003880781,0.0001187086,0.00003727062,0.000104323,0.0001016482,0.0000117412,0.0002105028,0.9956394,0.001254086,0.0002725974,0.00158509,0.00027655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0216506,0.0003748246,0.9748921,0.0001491892,0.001373334,0.0003821702,0.00001066156,0.0009886961,0.0001784481],"genre_scores_gemma":[0.9923816,0.0001277353,0.006173599,0.00002623638,0.0001074538,0.00009016463,0.00008234458,0.0001040802,0.0009068237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.970731,"threshold_uncertainty_score":0.9999803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777673619487854,"score_gpt":0.2810498466150821,"score_spread":0.2532731104202036,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}