{"id":"W3191656955","doi":"10.1109/icc42927.2021.9500370","title":"Simple and Efficient Algorithm for Drone Path Planning","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Drone; Motion planning; Computer science; Software deployment; Path (computing); Travelling salesman problem; Computation; Metric (unit); Energy consumption; Real-time computing; Task (project management); Simple (philosophy); Algorithm; Artificial intelligence; Engineering; Robot; Computer network; Operations management; 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":[0.000187978,0.0009998397,0.0006895816,0.0009445897,0.0008230719,0.0008299983,0.001347642,0.0009734106,0.007615193],"category_scores_gemma":[0.001140023,0.0004260433,0.0005839295,0.000964668,0.0004228635,0.001110291,0.001369038,0.001062747,0.002431492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005406118,"about_ca_system_score_gemma":0.001352453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004720481,"about_ca_topic_score_gemma":0.00685034,"domain_scores_codex":[0.999648,0.00003467299,0.00002313193,0.0001014577,0.0001590637,0.00003367108],"domain_scores_gemma":[0.9997185,0.00009132503,0.00002256972,0.00005332563,0.00009392335,0.00002044351],"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":[0.0001621285,0.0001403969,0.0005852639,0.0003336755,0.00006270468,0.0002168059,0.0001589641,0.3017901,0.01662589,0.0263651,0.01718201,0.6363769],"study_design_scores_gemma":[0.0001250301,0.0001012532,0.0002635321,0.00002990697,0.00002313296,0.0002379061,0.00004671676,0.9422461,0.006383717,0.01983891,0.03066678,0.00003696474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003835945,0.0003085114,0.9900845,0.00009402518,0.00007239833,0.0001667165,0.0001805553,0.001936177,0.003321064],"genre_scores_gemma":[0.05332626,0.0002918603,0.9412965,0.00006320444,0.00002411053,0.0003077993,0.0006015862,0.0001895482,0.003899223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007615193,"threshold_uncertainty_score":0.02547532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02152452679349274,"score_gpt":0.2736884696438729,"score_spread":0.2521639428503802,"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."}}