{"id":"W3034159639","doi":"10.1109/tcyb.2020.2994122","title":"Force-Based Algorithm for Motion Planning of Large Agent","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Benchmark (surveying); Collision avoidance; Position (finance); Scalability; Motion planning; Computer science; Overhead (engineering); Motion (physics); Collision; Algorithm; Term (time); State (computer science); Relative velocity; Distributed computing; Simulation; Artificial intelligence; Robot","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":[0.0003412939,0.001000672,0.000551216,0.0007340417,0.0007472315,0.0004809521,0.001345841,0.0007903749,0.003490387],"category_scores_gemma":[0.000810563,0.000312468,0.0004680447,0.0006620125,0.0005290693,0.0007508189,0.000960105,0.000862404,0.0007423536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008249183,"about_ca_system_score_gemma":0.001263144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00869407,"about_ca_topic_score_gemma":0.009484687,"domain_scores_codex":[0.999769,0.00002786754,0.00001143729,0.00005946623,0.0001061105,0.00002609857],"domain_scores_gemma":[0.9997922,0.00007746636,0.0000313001,0.00002647856,0.00005659118,0.00001593207],"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":[0.00006747714,0.00004995898,0.0003404858,0.00008915448,0.00003212443,0.0001145763,0.00007780353,0.8114281,0.005053745,0.01974822,0.004286845,0.1587114],"study_design_scores_gemma":[0.00001620644,0.00001561677,0.00004316051,0.000003937494,0.000003213265,0.00001853459,0.000006036514,0.9933571,0.0007672588,0.003196622,0.00256795,0.00000439182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002238742,0.0001182178,0.9956117,0.00005922353,0.00002787346,0.00004127252,0.00003273411,0.0005255761,0.001344636],"genre_scores_gemma":[0.1867298,0.0002924955,0.8079047,0.00009204393,0.0000474099,0.0004288633,0.0002886685,0.0001497246,0.00406638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00869407,"threshold_uncertainty_score":0.01728696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04034396080921998,"score_gpt":0.2792983518285214,"score_spread":0.2389543910193014,"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."}}