{"id":"W4415937960","doi":"10.36227/techrxiv.176238017.78871027/v1","title":"Safe Urban Delivery by a Mobile Robot via CLF-CBF-QP Control Under Dynamic Obstacles","year":2025,"lang":"","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Obstacle avoidance; Controller (irrigation); Kinematics; Mobile robot; Task (project management); Control (management); Obstacle; Function (biology); Robot; Jerk","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":[0.0004554982,0.0007068757,0.0004644421,0.0002792373,0.0004342735,0.0005803386,0.0009977544,0.0005251091,0.002043142],"category_scores_gemma":[0.001143641,0.0002296186,0.0003136789,0.0001703543,0.0005681168,0.0004503199,0.001486657,0.000800595,0.0003580125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005142684,"about_ca_system_score_gemma":0.001224026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006905692,"about_ca_topic_score_gemma":0.003977289,"domain_scores_codex":[0.9997484,0.00003727967,0.000008584863,0.0000608135,0.00009475099,0.00005017576],"domain_scores_gemma":[0.9996899,0.00009940692,0.00006404852,0.00002593239,0.00008342817,0.00003735853],"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":[0.0001200354,0.00007005909,0.0004788668,0.0001357298,0.00001562967,0.0001491083,0.0001889371,0.9038437,0.01459223,0.01373634,0.001847424,0.0648219],"study_design_scores_gemma":[0.00001201324,0.00006177613,0.00008795638,0.000005095224,0.000003005643,0.00001675756,0.00001277005,0.9960009,0.001193687,0.001767694,0.000834448,0.00000391118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01391117,0.00007773042,0.982393,0.0001088703,0.00003116274,0.00004913226,0.00002455366,0.0004849659,0.002919355],"genre_scores_gemma":[0.8897654,0.0001190144,0.1053988,0.00008100988,0.00002757742,0.0001963325,0.00008195995,0.0001063807,0.004223442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006905692,"threshold_uncertainty_score":0.013731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008868418700471314,"score_gpt":0.2497379288307713,"score_spread":0.2408695101303,"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."}}