{"id":"W4200160064","doi":"10.1109/ecice52819.2021.9645621","title":"Omnidirectional Platform for Autonomous Mobile Industrial Robot","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Holonomic; Mobile robot; Omnidirectional antenna; Robot; Kinematics; Computer vision; Computer science; Omnidirectional camera; Robot kinematics; Controller (irrigation); Artificial intelligence; Obstacle; Mobile manipulator; Robot control; Simulation; Antenna (radio)","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":[0.0001657093,0.0004414667,0.0002642341,0.0003142683,0.0002220553,0.000276781,0.0007269633,0.0003174688,0.004997364],"category_scores_gemma":[0.0001668441,0.0001428542,0.0002239814,0.0002178136,0.0001463145,0.0002509967,0.0005861665,0.0004112643,0.0032875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000164576,"about_ca_system_score_gemma":0.0004409271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000938898,"about_ca_topic_score_gemma":0.0009364613,"domain_scores_codex":[0.999824,0.00002230525,0.000008763859,0.00003053056,0.00009262586,0.00002180234],"domain_scores_gemma":[0.9999102,0.000007892754,0.00001113476,0.00001701533,0.00004005631,0.00001368676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003914667,0.0001470497,0.001726553,0.001119781,0.00003935886,0.001070609,0.0003452172,0.02368323,0.2608115,0.04346165,0.03599006,0.6312135],"study_design_scores_gemma":[0.000205527,0.001784659,0.004970626,0.0002102841,0.00007305612,0.003241509,0.0002013507,0.2116698,0.09241964,0.00766047,0.6774136,0.0001495239],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01897134,0.001401689,0.9213561,0.0002043244,0.0002938407,0.0002950522,0.0004603112,0.009116663,0.04790068],"genre_scores_gemma":[0.3421952,0.001374255,0.6008346,0.0002889339,0.00009742635,0.0009027339,0.00186439,0.0003773798,0.05206513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004997364,"threshold_uncertainty_score":0.01671785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04540549640295716,"score_gpt":0.2454638710136388,"score_spread":0.2000583746106816,"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."}}