{"id":"W4389665453","doi":"10.1109/iros55552.2023.10342150","title":"An MCTS-DRL Based Obstacle and Occlusion Avoidance Methodology in Robotic Follow-Ahead Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Obstacle avoidance; Computer science; Reinforcement learning; Collision avoidance; Robot; Obstacle; Artificial intelligence; Monte Carlo tree search; Mobile robot; Code (set theory); Process (computing); Tree (set theory); Monte Carlo method; Computer security; Operating system","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":[0.0004958329,0.0004659538,0.0005366111,0.000350596,0.0004508052,0.0004543666,0.001483234,0.000724193,0.00238707],"category_scores_gemma":[0.0009060545,0.0003874781,0.0004826887,0.0002444231,0.00056144,0.0007303875,0.00126394,0.0009871362,0.0005101198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005798684,"about_ca_system_score_gemma":0.001311426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004258142,"about_ca_topic_score_gemma":0.005633371,"domain_scores_codex":[0.9997476,0.00003590263,0.00001083486,0.00005352993,0.0001137753,0.00003834132],"domain_scores_gemma":[0.999739,0.00007604204,0.00004216054,0.00004627891,0.00006055325,0.00003590951],"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":[0.00008887187,0.00009220102,0.0008273068,0.0000888024,0.00003251447,0.000154213,0.0001430097,0.8071376,0.01865404,0.0176636,0.002520456,0.1525975],"study_design_scores_gemma":[0.000006395796,0.0000311663,0.00007743023,0.000004754195,0.000003491817,0.00002460999,0.00000679196,0.9943625,0.002261719,0.002179191,0.001036033,0.000005931413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01043821,0.000092067,0.9869251,0.00009892081,0.00001891,0.0000425615,0.0000301403,0.0006348389,0.001719247],"genre_scores_gemma":[0.4674733,0.000120327,0.5278078,0.0001205061,0.00001849376,0.0001377659,0.0001194732,0.0002006959,0.004001633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004258142,"threshold_uncertainty_score":0.008466721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09578545611895606,"score_gpt":0.3500192675301239,"score_spread":0.2542338114111678,"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."}}