{"id":"W3215473620","doi":"10.1109/ccdc52312.2021.9602221","title":"UAV Routine Optimization and Obstacle Avoidance Based on ACO for Transmission Line Inspection","year":2021,"lang":"en","type":"article","venue":"","topic":"Power Line Inspection Robots","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Development Research Centre","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Obstacle avoidance; Computer science; Collision avoidance; Obstacle; Kinematics; Transmission (telecommunications); Line (geometry); Range (aeronautics); Real-time computing; Computer vision; Simulation; Artificial intelligence; Engineering; Mobile robot; Aerospace engineering; Mathematics; Robot","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":[0.0001922004,0.0004486163,0.0005084598,0.0003840615,0.0002802823,0.0003698085,0.0005026911,0.00036671,0.000772395],"category_scores_gemma":[0.0005434803,0.000240107,0.000416736,0.000440875,0.0002605849,0.0003782766,0.0003223778,0.0003663268,0.0001203943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003338585,"about_ca_system_score_gemma":0.0006324102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008505019,"about_ca_topic_score_gemma":0.006351687,"domain_scores_codex":[0.999861,0.0000336724,0.000005597155,0.00003326828,0.00003984008,0.00002659919],"domain_scores_gemma":[0.9998447,0.00005902752,0.0000327643,0.00001260582,0.00003835192,0.00001255181],"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":[0.00005020365,0.00003980027,0.0009080911,0.00005596286,0.00003922771,0.00008587517,0.0000314828,0.9538526,0.002860336,0.003364072,0.0006741399,0.03803832],"study_design_scores_gemma":[0.000004926017,0.0000192149,0.0001478738,0.000001573309,0.000004825977,0.00001430217,0.000005561785,0.9989876,0.0002060433,0.000331695,0.0002742887,0.000001969656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0896368,0.0007402896,0.8999106,0.0001780163,0.00006976075,0.00006306639,0.00004768871,0.0004427855,0.008910911],"genre_scores_gemma":[0.8908014,0.0003567577,0.104567,0.00005117519,0.00002507713,0.00008908012,0.00008919769,0.00004834342,0.003971893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008505019,"threshold_uncertainty_score":0.01691103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008934907572873086,"score_gpt":0.2189797929096931,"score_spread":0.2100448853368201,"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."}}