{"id":"W3122155305","doi":"10.2316/j.2021.206-0610","title":"OBSTACLE AVOIDANCE FOR MULTI-UAV SYSTEM WITH OPTIMIZED ARTIFICIAL POTENTIAL FIELD ALGORITHM","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Aerospace Engineering and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Obstacle avoidance; Computer science; Potential field; Obstacle; Field (mathematics); Algorithm; Artificial intelligence; Mathematics; Mobile robot; Physics; Robot","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002857276,0.0005684718,0.0007310591,0.0003424744,0.0004162514,0.0005640733,0.0008145245,0.000759113,0.001308578],"category_scores_gemma":[0.0004349967,0.000275625,0.0004004998,0.0002899816,0.000345271,0.0005209119,0.0007525105,0.0005004217,0.0001661566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003708554,"about_ca_system_score_gemma":0.0007054617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004470128,"about_ca_topic_score_gemma":0.001972136,"domain_scores_codex":[0.9998748,0.00003255636,0.000005546572,0.00002092504,0.00004041296,0.00002567895],"domain_scores_gemma":[0.9998441,0.00006506083,0.00002519753,0.000006651912,0.00004341647,0.0000156634],"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":[0.00003116221,0.00001151415,0.0002330053,0.00002630598,0.00001566935,0.00005299197,0.00002118911,0.9854914,0.001333014,0.002178871,0.0003794459,0.0102254],"study_design_scores_gemma":[0.000003804219,0.000009952535,0.00003673043,0.000001410014,0.000001454597,0.000004753963,0.000002166,0.9994068,0.00006140817,0.0003688962,0.0001011387,0.000001505764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04899706,0.0008537924,0.9406081,0.000316261,0.00009787698,0.00004557036,0.00004369172,0.0002233781,0.008814181],"genre_scores_gemma":[0.9274316,0.0003102384,0.06727983,0.00009202307,0.0000383206,0.0001583893,0.00008946418,0.00003304873,0.004566902],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004470128,"threshold_uncertainty_score":0.008888245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008198139425394397,"score_gpt":0.2196009070049453,"score_spread":0.2114027675795509,"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."}}