{"id":"W2104733322","doi":"10.1109/robot.2002.1013426","title":"A solution to vicinity problem of obstacles in complete coverage path planning","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Motion planning; Workspace; Path (computing); Robot; Cover (algebra); Artificial neural network; Computer science; Obstacle; Planner; Mobile robot; Artificial intelligence; Engineering; Geography; Computer network","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":[0.0005159815,0.0001107374,0.0002003742,0.0001528104,0.00004901196,0.00003592801,0.0003657093,0.00004244584,0.00000572099],"category_scores_gemma":[0.00007814479,0.0001043539,0.0000274047,0.0005119207,0.00001701195,0.0002105264,0.00009928952,0.0001089224,0.00002453689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005661041,"about_ca_system_score_gemma":0.00006813027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001825541,"about_ca_topic_score_gemma":0.000006154517,"domain_scores_codex":[0.9987518,0.0001193536,0.0002997885,0.0002888521,0.0002413808,0.0002988437],"domain_scores_gemma":[0.9993502,0.0001263004,0.00008020123,0.0003107976,0.0000495662,0.00008292135],"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":[0.0000442967,0.0008304343,0.2048103,0.0002340345,0.00005759527,0.0004030651,0.02540423,0.4920512,0.03429182,0.2059767,0.004951522,0.03094485],"study_design_scores_gemma":[0.002027852,0.0006888236,0.1868863,0.0006246008,0.000007821161,0.00008306086,0.00028405,0.7824048,0.006902521,0.01532862,0.003853763,0.0009078328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04079101,0.00003507899,0.9492799,0.0001234543,0.00009792535,0.000207934,0.00000269139,0.00008353178,0.009378472],"genre_scores_gemma":[0.5397631,6.093264e-7,0.4600199,0.0001460358,0.000004642763,0.000006210937,0.000001057279,0.000003673497,0.0000547444],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4989721,"threshold_uncertainty_score":0.425543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04176722436237148,"score_gpt":0.2712663492207149,"score_spread":0.2294991248583434,"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."}}