{"id":"W2916095269","doi":"10.2316/j.2019.206-0088","title":"MULTI-OBJECTIVE TRAJECTORY PLANNING OF ROBOT MANIPULATOR IN A MOVING OBSTACLE ENVIRONMENT","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Obstacle; Trajectory; Robot; Computer science; Obstacle avoidance; Manipulator (device); Robot manipulator; Motion planning; Artificial intelligence; Mobile robot; Geography; Physics","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":[0.0003425118,0.00008568497,0.0001759114,0.0002949006,0.00001522242,0.00005175655,0.000349316,0.00004405149,0.000003856948],"category_scores_gemma":[0.00004504261,0.00008155969,0.00004602438,0.00007319348,0.00001682022,0.0004738506,0.00008308207,0.0001373216,0.000003511674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203473,"about_ca_system_score_gemma":0.00004798562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001088514,"about_ca_topic_score_gemma":3.549876e-7,"domain_scores_codex":[0.9988703,0.0000446672,0.0004466483,0.0001270083,0.0004102804,0.0001011152],"domain_scores_gemma":[0.9991873,0.0001064945,0.0004673146,0.0000957462,0.0001038401,0.00003933435],"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":[0.000008649503,0.00007941666,0.03004018,0.00001035642,0.0000422821,0.0000410578,0.001532466,0.9565611,0.006274825,0.0005393057,0.000003607214,0.004866706],"study_design_scores_gemma":[0.0006081568,0.00007308373,0.2798171,0.0001793803,0.000003909735,0.00008415434,0.0001027805,0.7182754,0.0006103946,0.0001758931,0.000005035378,0.00006463689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3194563,0.0001646787,0.6795481,0.0001484115,0.0005757173,0.00006556124,0.000001022977,0.000009277929,0.00003090375],"genre_scores_gemma":[0.7012689,0.00001059038,0.2986517,0.0000192405,0.00002818733,4.794391e-7,8.834301e-7,0.000004106837,0.00001599938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3818125,"threshold_uncertainty_score":0.3325908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01888058713334327,"score_gpt":0.261489482684636,"score_spread":0.2426088955512927,"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."}}