{"id":"W186670194","doi":"10.1007/978-1-84800-155-8_11","title":"Getting Mobile Autonomous Robots to Form a Prescribed Geometric Arrangement","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in control and information sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Rendezvous; Task (project management); Mobile robot; Supervisor; Computer science; Polygon (computer graphics); Point (geometry); Antenna (radio); Control engineering; Control theory (sociology); Control (management); Engineering; Artificial intelligence; Mathematics; Telecommunications; Geometry","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":[0.0001469375,0.0005540744,0.0004090066,0.0002577842,0.0004929362,0.0004827314,0.000600411,0.000606839,0.006684551],"category_scores_gemma":[0.0007695133,0.0005021034,0.0004899463,0.0003456355,0.0006672964,0.0009933179,0.001471142,0.0006508218,0.002922715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002779732,"about_ca_system_score_gemma":0.0004839328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009003695,"about_ca_topic_score_gemma":0.001811586,"domain_scores_codex":[0.9998436,0.00001294194,0.0000075768,0.00004137852,0.00007319672,0.00002118925],"domain_scores_gemma":[0.9998728,0.00002659788,0.00001716193,0.00004880068,0.00001869429,0.00001589723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002264727,0.00009735786,0.001199064,0.0003539146,0.00003487096,0.0003656026,0.0009798198,0.2766551,0.1178131,0.10273,0.007706414,0.4918382],"study_design_scores_gemma":[0.0001373172,0.0006082839,0.001381159,0.00008684539,0.00003915313,0.0007298285,0.0007349023,0.720572,0.07369222,0.1373308,0.0646338,0.0000537646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0532335,0.00006116601,0.9312094,0.0001086708,0.00005211648,0.00008503503,0.00006307081,0.001105293,0.01408173],"genre_scores_gemma":[0.2667034,0.0002105351,0.7097017,0.00005782337,0.00001925602,0.0001461388,0.0003040843,0.000351404,0.02250577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006684551,"threshold_uncertainty_score":0.02236199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02170990503951657,"score_gpt":0.2625317937273399,"score_spread":0.2408218886878233,"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."}}