{"id":"W4254715918","doi":"10.32920/ryerson.14643939","title":"Implementation of SLAM, Navigation, Obstacle Avoidance, and Path Planning of a Robust Mobile Robot Using 2D Laser Scanner","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Simultaneous localization and mapping; Mobile robot; Obstacle avoidance; Motion planning; Artificial intelligence; Computer science; Computer vision; Robot; Sonar; Computation; Obstacle; Key (lock); Laser scanning; Laser; Geography; Computer security","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":[0.0002383244,0.0004942931,0.0004050213,0.0003133887,0.0002634547,0.0003881612,0.0006476602,0.0004363984,0.002220528],"category_scores_gemma":[0.0004931023,0.0002792743,0.0003930919,0.0002356689,0.0002113666,0.0004054926,0.0004989792,0.0003887074,0.000832043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002599866,"about_ca_system_score_gemma":0.0009399031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002638388,"about_ca_topic_score_gemma":0.002247911,"domain_scores_codex":[0.999743,0.00002790136,0.00001366955,0.00006115491,0.0001151539,0.00003904274],"domain_scores_gemma":[0.9998426,0.00002418398,0.00001835128,0.00005005374,0.0000520144,0.00001275407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003203175,0.000186256,0.002379935,0.0003357168,0.00008835064,0.0005557014,0.0002756171,0.2624539,0.3541771,0.005598527,0.002264339,0.3713642],"study_design_scores_gemma":[0.00007572602,0.0006604252,0.003201263,0.00002255514,0.00003868445,0.0003660232,0.00009565722,0.7523597,0.233726,0.001378927,0.008029587,0.00004548459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.165954,0.0001866379,0.8233134,0.00009113595,0.00006690477,0.0002153086,0.0002037319,0.004949625,0.00501921],"genre_scores_gemma":[0.5818917,0.0001300875,0.4147044,0.0000266161,0.000008752818,0.0001833767,0.0002818949,0.0001012323,0.002672016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002638388,"threshold_uncertainty_score":0.007428467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179861842832969,"score_gpt":0.2697766847264536,"score_spread":0.2479780662981239,"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."}}