{"id":"W2081505722","doi":"10.1109/mesa.2010.5551989","title":"Motion planning for multi-link robots using Artificial Potential Fields and modified Simulated Annealing","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Maxima and minima; Motion planning; Simulated annealing; Robot; Computer science; Path (computing); Motion control; Adaptive simulated annealing; Convergence (economics); Artificial intelligence; Mathematical optimization; Algorithm; Mathematics","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":[0.0007911134,0.0005760119,0.0006503672,0.000500866,0.0004216224,0.0004754685,0.001178389,0.0009755614,0.0009020421],"category_scores_gemma":[0.0016512,0.0004551783,0.00070041,0.0003695578,0.001018326,0.0007221021,0.0007356103,0.0006713838,0.0001325458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006292313,"about_ca_system_score_gemma":0.0006170992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001998962,"about_ca_topic_score_gemma":0.00156458,"domain_scores_codex":[0.9996754,0.0001308495,0.00001516136,0.00004916275,0.0001061186,0.00002345359],"domain_scores_gemma":[0.9994186,0.0003802976,0.00006644952,0.00004225142,0.00006672397,0.00002566675],"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":[0.00001954972,0.0000146467,0.0001363234,0.00002987326,0.00002113783,0.00003602372,0.00003562689,0.9757531,0.002326867,0.00731469,0.0001153879,0.01419681],"study_design_scores_gemma":[0.000006407969,0.00002006785,0.0000334643,0.000002429094,0.000002228781,0.0000082906,0.00000273913,0.9974395,0.0002688559,0.001930626,0.0002821006,0.000003287498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01641355,0.0001645049,0.9819301,0.00008627486,0.00002535299,0.00003502541,0.000007516002,0.0001095359,0.001228226],"genre_scores_gemma":[0.5837742,0.0002270978,0.4133053,0.00009593329,0.0000315241,0.0004068491,0.00004100851,0.00007581209,0.002042325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001998962,"threshold_uncertainty_score":0.004565418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007424060873455,"score_gpt":0.3371344865068429,"score_spread":0.2363920804194974,"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."}}