{"id":"W4391054243","doi":"10.2316/j.2024.206-1055","title":"BEHAVIOUR-DEFINED NAVIGATION FRAMEWORK FOR DYNAMICAL OBSTACLE AVOIDANCE IN MULTI-ROBOT SYSTEMS CONSISTING OF HOLONOMIC ROBOTS, 379-390.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Holonomic; Obstacle avoidance; Robot; Obstacle; Computer science; Collision avoidance; Mobile robot; Artificial intelligence; Control engineering; Computer vision; Control theory (sociology); Engineering; Geography; Computer security; Control (management); Collision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006931046,0.00100013,0.0005351459,0.0007161123,0.0005379005,0.0008141071,0.001628427,0.0008851354,0.001638135],"category_scores_gemma":[0.0008745194,0.000370474,0.001044445,0.0003887457,0.001122409,0.0009483728,0.001477924,0.001102145,0.0005885566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200695,"about_ca_system_score_gemma":0.001484602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006217125,"about_ca_topic_score_gemma":0.006812463,"domain_scores_codex":[0.9996443,0.00008777535,0.00002368555,0.00007145572,0.0001362347,0.00003654303],"domain_scores_gemma":[0.9997448,0.00006564005,0.00004439654,0.00002384594,0.00008661786,0.00003462288],"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":[0.00005948425,0.00006825714,0.0008807286,0.0002222882,0.00006499051,0.0003631156,0.0003692311,0.6619124,0.0106072,0.2737002,0.002343565,0.04940858],"study_design_scores_gemma":[0.00001101519,0.00006769492,0.0001775667,0.00002097199,0.0000136083,0.00006915283,0.00003973568,0.9673253,0.0006649875,0.0273135,0.004282466,0.00001408674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001710139,0.0001573178,0.9964574,0.0000541024,0.00002831881,0.00002265995,0.00001817111,0.00007963557,0.001472216],"genre_scores_gemma":[0.4033168,0.0008515046,0.586176,0.0001705323,0.00009965816,0.0006403644,0.0003038357,0.000162706,0.008278519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006217125,"threshold_uncertainty_score":0.01236188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03261786131653167,"score_gpt":0.3218643071443674,"score_spread":0.2892464458278357,"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."}}