{"id":"W2160609653","doi":"10.5772/54933","title":"Mobile Robot Collision Avoidance in Human Environments","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Hatch (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Collision avoidance; Robot; Mobile robot; Holonomic; Obstacle avoidance; Piecewise; Nonholonomic system; Artificial intelligence; Motion (physics); Motion planning; Collision; Simulation; Computer vision; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004535588,0.0001825315,0.0003598305,0.0003753873,0.00005480949,0.0002192381,0.001771049,0.00007273811,0.00000993408],"category_scores_gemma":[0.00009016876,0.0001622288,0.00009264772,0.0002049634,0.00004064865,0.001409635,0.0001777397,0.00028494,0.00008220846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004050073,"about_ca_system_score_gemma":0.00005506114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005916431,"about_ca_topic_score_gemma":6.250561e-7,"domain_scores_codex":[0.9973508,0.0001321835,0.0009684094,0.0002721229,0.0009971047,0.0002793582],"domain_scores_gemma":[0.9983937,0.0001431028,0.000767199,0.0003201135,0.0002397038,0.0001362209],"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":[0.00000493691,0.0001186313,0.001617121,0.000006039505,0.00003802113,0.0001736008,0.0003707049,0.9794998,0.01388293,0.0005421644,0.0001830927,0.003563025],"study_design_scores_gemma":[0.005823349,0.001272084,0.0518319,0.002472774,0.00001912851,0.002465101,0.001012133,0.9229592,0.005046332,0.002839644,0.003302873,0.0009554623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05893651,0.0009425035,0.9336419,0.0002046546,0.005533319,0.0003921587,0.000001155423,0.00002750053,0.0003202899],"genre_scores_gemma":[0.8923116,0.00005920638,0.1065655,0.00006069359,0.000258449,0.00003776671,0.000002035642,0.00001527817,0.0006894848],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8333751,"threshold_uncertainty_score":0.6615499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075867858943218,"score_gpt":0.2673850666620646,"score_spread":0.2566263880726324,"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."}}