{"id":"W2979970169","doi":"10.1109/ccece.2019.8861921","title":"A GPU Accelerated Path Planner for Multiple Unmanned Aerial Vehicles","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Motion planning; Real-time computing; Path (computing); Terrain; Point of interest; Planner; Acceleration; Task (project management); Process (computing); Shortest path problem; Simulation; Artificial intelligence; Robot; Operating system; Graph; 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":[0.0001333072,0.0003613249,0.0002684857,0.0002717393,0.000322966,0.0004075863,0.0005731821,0.0003689356,0.001984851],"category_scores_gemma":[0.0004010423,0.0002124617,0.0002762817,0.0003170103,0.0002084333,0.0002307737,0.0002954654,0.0005069079,0.0002756694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006835229,"about_ca_system_score_gemma":0.001474776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0278327,"about_ca_topic_score_gemma":0.0293284,"domain_scores_codex":[0.9999104,0.00001508142,0.000003191773,0.00002165834,0.00003775865,0.00001186747],"domain_scores_gemma":[0.9999207,0.00002671743,0.000006067419,0.000009058369,0.00002881985,0.000008540503],"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":[0.0001412805,0.00005211277,0.0008505286,0.00007659182,0.00002641324,0.0001418093,0.0001165249,0.8264322,0.01413132,0.006707997,0.003360198,0.1479631],"study_design_scores_gemma":[0.0000132619,0.00002634848,0.0001410963,0.000002759293,0.000002715449,0.00001748346,0.00001154594,0.995617,0.001469347,0.0006537845,0.002041464,0.00000320564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06683721,0.0002140781,0.9221218,0.0001867678,0.00007041466,0.0001304996,0.0002081258,0.003175869,0.007055257],"genre_scores_gemma":[0.3258258,0.0001259795,0.6689636,0.00003428952,0.000008216435,0.0001224897,0.0003312764,0.0001494202,0.004439014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0278327,"threshold_uncertainty_score":0.05534136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03190223287727267,"score_gpt":0.2591881556539167,"score_spread":0.227285922776644,"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."}}