{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040162,0.0004941075,0.0004918189,0.0003893989,0.0005526703,0.0003789569,0.0005664828,0.000470125,0.0006476078],"category_scores_gemma":[0.001551485,0.0002164607,0.000260875,0.0003244835,0.000572069,0.0005402667,0.0006597407,0.0003273071,0.0001681691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002999221,"about_ca_system_score_gemma":0.0004878078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003550356,"about_ca_topic_score_gemma":0.002101186,"domain_scores_codex":[0.9997249,0.00007838852,0.000009199919,0.00004673369,0.0001101599,0.00003067351],"domain_scores_gemma":[0.9995399,0.0002338499,0.00007426893,0.0000458217,0.00008786241,0.00001846182],"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":[0.0001846091,0.00003977899,0.0009611418,0.0001137725,0.00004665709,0.0003533731,0.0003616336,0.819645,0.02745862,0.00944212,0.001086544,0.1403066],"study_design_scores_gemma":[0.0000194757,0.0001545382,0.000638713,0.00001346626,0.00001338706,0.0002518455,0.0000732985,0.977576,0.01004941,0.007850369,0.003340153,0.00001941662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05961778,0.0004140979,0.9378498,0.00006319163,0.00002657372,0.00002702424,0.00001312527,0.0004779533,0.001510501],"genre_scores_gemma":[0.7991848,0.0003177591,0.1983176,0.0000541357,0.00001709813,0.00006601022,0.00003778351,0.00005585559,0.001948921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003550356,"threshold_uncertainty_score":0.007059395,"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."}}