{"id":"W1969098212","doi":"10.1017/s0263574714000939","title":"Simulation-based fast collision detection for scaled polyhedral objects in motion by exploiting analytical contact equations","year":2014,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Council","keywords":"Scaling; Collision detection; Collision; Regular polygon; Algorithm; Polyhedron; Motion (physics); Rotation (mathematics); Computer science; Translation (biology); Feature (linguistics); Mathematics; Geometry; Artificial intelligence","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":[0.0005804656,0.0004221162,0.0006244968,0.0006192364,0.0004550801,0.0005552338,0.0009139494,0.0004319991,0.001077708],"category_scores_gemma":[0.002281044,0.00045432,0.0004613226,0.0004076291,0.0009838879,0.0005757164,0.000979088,0.0005189333,0.0001229928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009226371,"about_ca_system_score_gemma":0.001051917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004969745,"about_ca_topic_score_gemma":0.00240352,"domain_scores_codex":[0.9995839,0.00008703197,0.00002228682,0.00005843813,0.0002074507,0.00004093998],"domain_scores_gemma":[0.9991161,0.0004660782,0.0001070289,0.0001162891,0.0001530877,0.00004135953],"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":[0.0000441892,0.0000175283,0.0006466766,0.00002443184,0.000008856014,0.00005439238,0.00006302969,0.9683822,0.00553926,0.00806865,0.0001445419,0.01700623],"study_design_scores_gemma":[0.000001988569,0.000004350988,0.00003028393,8.049624e-7,6.174398e-7,0.000003796751,0.0000024725,0.9987882,0.000531703,0.0005264169,0.0001080342,0.000001328407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04878782,0.00005180895,0.9499094,0.00004383044,0.0000108942,0.00003698321,0.000010853,0.000247211,0.0009012767],"genre_scores_gemma":[0.7738518,0.00008240373,0.2250396,0.0000188682,0.00000770317,0.000103182,0.00004434196,0.0000570843,0.0007949324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004969745,"threshold_uncertainty_score":0.009881616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458500469895364,"score_gpt":0.2833142536868941,"score_spread":0.2587292489879405,"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."}}