{"id":"W4396855513","doi":"10.3390/s24103100","title":"Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shanghai","keywords":"Obstacle avoidance; Iterative closest point; Computer science; Robot; Computer vision; Point cloud; Obstacle; Artificial intelligence; Motion planning; Mobile robot; Overshoot (microwave communication); Path (computing); Collision avoidance; Trajectory; Collision; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002818737,0.0001503629,0.0001454974,0.0001705286,0.0001948672,0.0003450577,0.0001555109,0.00006055008,0.000002083839],"category_scores_gemma":[0.00007696157,0.0001415341,0.00002168723,0.0002844343,0.0000369225,0.0002144919,0.00008239406,0.0002058904,0.00001973018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003073632,"about_ca_system_score_gemma":0.00002534957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001178919,"about_ca_topic_score_gemma":8.023586e-8,"domain_scores_codex":[0.9987407,0.00007459336,0.0001622171,0.000527598,0.0002422334,0.0002526865],"domain_scores_gemma":[0.9990937,0.0004927677,0.00003946143,0.0002586302,0.00002429186,0.00009113268],"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":[0.0000603208,0.00008620619,0.001349389,0.0004333012,0.00008150253,0.002285241,0.02120823,0.5701347,0.0543094,0.01495666,0.001150545,0.3339445],"study_design_scores_gemma":[0.000187689,0.00008738388,0.008135592,0.0009839376,0.000003764446,0.00003444149,0.00008990313,0.9859737,0.003481959,0.0007417253,0.00009257947,0.0001873365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4437105,0.0004847266,0.5541248,0.0003914679,0.0004191495,0.00008366897,0.000003377593,0.0002795207,0.0005027556],"genre_scores_gemma":[0.8210685,0.000006148387,0.178577,0.0001660377,0.00005946379,0.000003922823,0.000002904485,0.00001186216,0.0001042103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.415839,"threshold_uncertainty_score":0.5771591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361732969905327,"score_gpt":0.2458808329737931,"score_spread":0.2322635032747398,"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."}}