{"id":"W4392543142","doi":"10.32920/25365352.v1","title":"A Multi-Objective Trajectory Planning Method for Collaborative Robot","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing Information Science and Technology University; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Robot; Motion planning; Human–computer interaction; Artificial intelligence; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007329094,0.0009062643,0.0006247364,0.0006428475,0.0007747315,0.0005968963,0.001060333,0.0008123051,0.003037777],"category_scores_gemma":[0.0009405046,0.0003972832,0.0007499137,0.0007024401,0.0004345327,0.0007163631,0.0009285242,0.0008601738,0.0004492581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007291398,"about_ca_system_score_gemma":0.00138039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008821868,"about_ca_topic_score_gemma":0.004535748,"domain_scores_codex":[0.999622,0.00007809333,0.00002014228,0.00009029969,0.0001554633,0.00003395195],"domain_scores_gemma":[0.9996997,0.00008571646,0.00003782685,0.00002696661,0.0001252876,0.0000246199],"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":[0.00005209649,0.00003467179,0.0002930278,0.0001099848,0.00003826685,0.00008345719,0.0001284508,0.8558452,0.004702833,0.01131189,0.001533128,0.1258671],"study_design_scores_gemma":[0.000008339367,0.00002974745,0.0000535916,0.000005551827,0.000004559736,0.00002042067,0.00001014427,0.9965648,0.0007934886,0.00139608,0.001107153,0.000006148126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001866905,0.00005622232,0.996798,0.00003094084,0.00001679932,0.00002391831,0.00001053045,0.0001185932,0.001078117],"genre_scores_gemma":[0.2879054,0.0002411406,0.7046337,0.00006099049,0.00003815166,0.0004212876,0.0001423235,0.0001140358,0.006442986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008821868,"threshold_uncertainty_score":0.01754105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06796582472776573,"score_gpt":0.3649779583139575,"score_spread":0.2970121335861918,"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."}}