{"id":"W2057933286","doi":"10.1016/j.robot.2005.09.002","title":"Probabilistic Road Map sampling strategies for multi-robot motion planning","year":2005,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Probabilistic roadmap; Robot; Mobile robot; Motion planning; Probabilistic logic; Odometry; Artificial intelligence; Distributed computing; Configuration space; Real-time computing","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":[0.001629466,0.0008663899,0.001674528,0.001259728,0.0007395106,0.0008734513,0.002704098,0.001173954,0.002475567],"category_scores_gemma":[0.00647876,0.001066417,0.0007565349,0.001550609,0.0008490284,0.002202811,0.001686075,0.001205067,0.0004874246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009433806,"about_ca_system_score_gemma":0.001030635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007208276,"about_ca_topic_score_gemma":0.01004599,"domain_scores_codex":[0.9991555,0.0003013682,0.00004024966,0.0001473988,0.0002829932,0.00007242487],"domain_scores_gemma":[0.997183,0.001961414,0.0001684526,0.0002519433,0.0003427412,0.0000925032],"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":[0.0001827021,0.00007417837,0.0004847872,0.00009910717,0.00005615775,0.00004720531,0.00008094934,0.8806978,0.001426519,0.01936841,0.001528101,0.09595415],"study_design_scores_gemma":[0.00001125835,0.00001468718,0.00005360275,0.000003610834,0.000005217643,0.000009938299,0.00000422122,0.9922073,0.0003205142,0.007107722,0.0002572863,0.000004625618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006705831,0.0002038638,0.9920941,0.00004774171,0.00001648697,0.00003789557,0.00004105604,0.0002353459,0.0006176478],"genre_scores_gemma":[0.4946195,0.0004123507,0.5009809,0.0001222449,0.00006536321,0.0004919138,0.0003704102,0.0002495556,0.002687761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007208276,"threshold_uncertainty_score":0.01433265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0776086684401754,"score_gpt":0.3077473435480166,"score_spread":0.2301386751078413,"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."}}