{"id":"W4416371853","doi":"10.2316/j.2026.206-1234","title":"EFFICIENT COLLISION AVOIDANCE AND MOTION PLANNING FOR INDUSTRIAL ROBOTS BASED ON NSGA-II AND GJK. 124-138","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Robot; Collision avoidance; Motion planning; Motion (physics); Industrial robot; Collision; Context (archaeology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002756011,0.00009257627,0.0001269514,0.000221169,0.00008638311,0.00009573252,0.0000695355,0.00007015638,0.000001705202],"category_scores_gemma":[0.0001090411,0.00008295709,0.00002436497,0.00005799414,0.00002035388,0.00007268376,0.00002091817,0.0001189439,1.572299e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005756989,"about_ca_system_score_gemma":0.00001984448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001581554,"about_ca_topic_score_gemma":6.376141e-7,"domain_scores_codex":[0.999325,0.00001578176,0.0002947501,0.00009240318,0.0001934766,0.00007858092],"domain_scores_gemma":[0.9995176,0.000128329,0.0001105775,0.00004154939,0.0001626693,0.0000393337],"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":[0.00004429713,0.00002342169,0.0004131246,0.00002260557,0.00002936738,0.000002634905,0.00009709011,0.9646918,0.0006843123,0.001873856,0.0001434616,0.03197408],"study_design_scores_gemma":[0.0007194104,0.0001213453,0.003906399,0.0004272026,0.00001916022,0.0000119182,0.00002512652,0.992465,0.001485486,0.000463645,0.0002820774,0.00007324043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3363783,0.0002360842,0.6616936,0.0006287156,0.0008458062,0.0001284864,0.000006061771,0.00001643054,0.00006648996],"genre_scores_gemma":[0.9928585,0.00005954469,0.006850407,0.00006364549,0.000127285,0.000003142684,0.000005811928,0.000007350302,0.00002435183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6564801,"threshold_uncertainty_score":0.3382892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184987704336272,"score_gpt":0.2684457899500992,"score_spread":0.249947019516472,"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."}}