{"id":"W2566278123","doi":"10.1109/iros.2016.7759499","title":"The constriction decomposition method for coverage path planning","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Decomposition; Heuristic; Computer science; Motion planning; Path (computing); Constriction; Decomposition method (queueing theory); Mathematical optimization; Task (project management); Algorithm; Artificial intelligence; Mathematics; Engineering; Computer network; Systems engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004708087,0.0006824092,0.0005738252,0.0008114422,0.0004909725,0.0004944029,0.0006347232,0.0006741513,0.003471642],"category_scores_gemma":[0.001805616,0.0004397668,0.0006673104,0.0008039071,0.0005883931,0.0009517799,0.001170592,0.001208432,0.0006568801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005438803,"about_ca_system_score_gemma":0.0006982313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001978761,"about_ca_topic_score_gemma":0.002032803,"domain_scores_codex":[0.9995671,0.0001140037,0.00001446471,0.00008432719,0.0001773828,0.00004275439],"domain_scores_gemma":[0.9995008,0.0003025276,0.00004176639,0.00006153517,0.00006040036,0.00003292648],"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":[0.0001097906,0.00004878155,0.0005878449,0.0002493692,0.00002955257,0.000163914,0.0001740554,0.7235684,0.01345739,0.04929119,0.006480985,0.2058387],"study_design_scores_gemma":[0.00001720787,0.00004754274,0.0001718907,0.00002392599,0.000006712992,0.0001098416,0.00002092594,0.9784656,0.002078555,0.01238678,0.006658818,0.00001213107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003072233,0.0001897023,0.9945093,0.0000564516,0.00001865013,0.00002002738,0.00004318779,0.0001466486,0.001943853],"genre_scores_gemma":[0.1689437,0.0006310391,0.8259581,0.00009439467,0.00006663433,0.0002716137,0.0003565112,0.0001583088,0.003519717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003471642,"threshold_uncertainty_score":0.01161379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154157062578772,"score_gpt":0.3246225056569995,"score_spread":0.3030809350312118,"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."}}