{"id":"W1674276221","doi":"10.1109/icinfa.2015.7279550","title":"The obstacle detection and obstacle avoidance algorithm based on 2-D lidar","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Obstacle avoidance; Obstacle; Mobile robot; Computer science; Collision avoidance; Computer vision; Lidar; Artificial intelligence; Cluster analysis; Point cloud; Robot; Algorithm; Collision; Remote sensing; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006570946,0.0001409261,0.0001096669,0.00005439613,0.0003102081,0.0003065367,0.0005145764,0.00005656873,0.000001144491],"category_scores_gemma":[0.0001974716,0.00009715578,0.00002844052,0.0003001099,0.00006825491,0.0002767897,0.0001093344,0.0001743119,0.00008368838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007212097,"about_ca_system_score_gemma":0.00008037491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005576339,"about_ca_topic_score_gemma":0.000004684603,"domain_scores_codex":[0.9986246,0.0001135431,0.0001654621,0.0003705495,0.0004095122,0.0003163057],"domain_scores_gemma":[0.9986606,0.0003681446,0.00006581133,0.0006139908,0.0001011933,0.0001903167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001149866,0.00005131118,0.000214505,0.000003409247,0.000008416177,0.00003293807,0.0004412056,0.005215758,0.000179624,0.001267477,0.0009600866,0.9916137],"study_design_scores_gemma":[0.0004329781,0.0002168241,0.002365371,0.00001114863,0.00000228341,0.0000262389,0.00006992019,0.9879572,0.002340977,0.001016511,0.005411052,0.0001495383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002267524,0.0001639534,0.9928331,0.001114643,0.000799593,0.0001420598,0.000001267328,0.0002995352,0.002378305],"genre_scores_gemma":[0.3324723,0.000006151518,0.6646749,0.0009498011,0.0001487268,0.00003131602,9.847497e-7,0.00001907989,0.001696738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9914643,"threshold_uncertainty_score":0.3961898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02213955529363215,"score_gpt":0.2399137672042231,"score_spread":0.217774211910591,"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."}}