{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004695124,0.0005318327,0.0003618121,0.0003848816,0.0003188225,0.00044318,0.0009845119,0.0002905105,0.002488921],"category_scores_gemma":[0.00156653,0.0002763338,0.0002401119,0.0002233908,0.0002956615,0.0004616107,0.0006217525,0.0004678865,0.0004431857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004090181,"about_ca_system_score_gemma":0.001263423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003514252,"about_ca_topic_score_gemma":0.003865026,"domain_scores_codex":[0.9998017,0.00002990245,0.00001417632,0.00005347236,0.00006299381,0.00003774188],"domain_scores_gemma":[0.9995738,0.000122963,0.0000540262,0.00007515389,0.0001149386,0.0000591137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004421899,0.0002542489,0.004823802,0.0001130177,0.00005406249,0.00009871842,0.0001948269,0.6562383,0.01654746,0.007942327,0.004765346,0.3085256],"study_design_scores_gemma":[0.00004162705,0.000107246,0.0002781107,0.000004307101,0.000008803152,0.00001928212,0.00001259334,0.9904786,0.005429165,0.001449286,0.002161322,0.000009672198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06234014,0.0001219515,0.9276858,0.0000581977,0.0000687049,0.0001365567,0.0001009074,0.006730898,0.002756946],"genre_scores_gemma":[0.685805,0.00007998076,0.3116632,0.00004034127,0.00002622877,0.0001652754,0.0002260105,0.0003359352,0.001658052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003514252,"threshold_uncertainty_score":0.008326232,"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."}}