{"id":"W2334016672","doi":"10.2514/6.2016-1984","title":"Inexpensive, Efficient, Light-weight Vision-based Collision Avoidance System for Small Unmanned Aerial Vehicles","year":2016,"lang":"en","type":"article","venue":"AIAA Infotech @ Aerospace","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collision avoidance; Computer science; Collision avoidance system; Computer vision; Machine vision; Remotely operated underwater vehicle; Artificial intelligence; Collision; Mobile robot; Robot; Computer security","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008096058,0.0004832878,0.0005612656,0.000249112,0.0004827486,0.000298604,0.001730547,0.0003416701,0.000002751707],"category_scores_gemma":[0.0005706191,0.0003510998,0.0002024267,0.000754451,0.0001172932,0.0003771946,0.0003799253,0.0002015509,0.0002549691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000344715,"about_ca_system_score_gemma":0.0002838444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004635232,"about_ca_topic_score_gemma":0.000005514371,"domain_scores_codex":[0.9965863,0.0001577957,0.000630941,0.001044142,0.0006591086,0.000921671],"domain_scores_gemma":[0.9960994,0.001081451,0.0004617996,0.001513505,0.0005363013,0.0003075169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001364601,0.001138209,0.002104495,0.0011238,0.0002784659,0.0005129996,0.002989608,0.1534121,0.6763774,0.05153239,0.06672513,0.04244085],"study_design_scores_gemma":[0.006811198,0.001138488,0.001437656,0.002117794,0.0000359853,0.00004429462,0.00007399687,0.4972746,0.4734481,0.0001692549,0.016143,0.001305663],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09013751,0.0001149213,0.9000364,0.00494516,0.002464096,0.001072619,0.00003767631,0.0009934566,0.0001981377],"genre_scores_gemma":[0.7072363,0.000006598497,0.2910077,0.0004277918,0.0004131449,0.0002211888,0.000009417679,0.00006755017,0.0006103282],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6170987,"threshold_uncertainty_score":0.9998941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239513753263717,"score_gpt":0.2351162129281792,"score_spread":0.222721075395542,"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."}}