{"id":"W2152548017","doi":"10.1109/crv.2014.16","title":"Speed Daemon: Experience-Based Mobile Robot Speed Scheduling","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Computer science; Scheduling (production processes); Motion planning; Speedup; Robot; Real-time computing; Ackermann function; Odometry; Terrain; Path (computing); Schedule; Electronic speed control; Simulation; Artificial intelligence; Mathematical optimization; Engineering; Computer network; Parallel computing; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004877586,0.0002418422,0.0002803947,0.0001443255,0.0001812359,0.0002807356,0.001427927,0.00009710217,0.00008283275],"category_scores_gemma":[0.0001360141,0.0002147156,0.00009068561,0.0005213306,0.00008223065,0.0004440914,0.0002229467,0.0001867527,0.0004832369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005053015,"about_ca_system_score_gemma":0.00008330704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005928078,"about_ca_topic_score_gemma":7.525901e-7,"domain_scores_codex":[0.9978225,0.0001049577,0.0003360237,0.0006778174,0.0005080804,0.0005506288],"domain_scores_gemma":[0.9982421,0.0002126351,0.000111488,0.001100902,0.00009445442,0.0002383674],"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":[0.000009762308,0.0001922849,0.003388114,0.00002353559,0.00001864681,0.00006450601,0.002184145,0.9335086,0.01358383,0.006015677,0.0006164737,0.04039447],"study_design_scores_gemma":[0.000485319,0.0001322744,0.0008408554,0.00003080812,0.000003201871,0.00001504032,0.00009081419,0.9781701,0.01881813,0.0002050937,0.0008935806,0.0003148105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04078251,0.00003469366,0.9483814,0.0003603666,0.0008651617,0.0002055808,4.009711e-7,0.0006995987,0.008670262],"genre_scores_gemma":[0.4546693,5.669373e-7,0.5440029,0.000593269,0.0001415204,0.000007366443,0.000002398859,0.00001227655,0.0005704109],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4138868,"threshold_uncertainty_score":0.8755848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02739532617823242,"score_gpt":0.2744155550016438,"score_spread":0.2470202288234113,"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."}}