{"id":"W2129604054","doi":"10.1109/robot.2001.932564","title":"On eye-sensor based path planning for robots with non-trivial geometry/kinematics","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Observability; Kinematics; Completeness (order theory); Reachability; Configuration space; Robot; Computer science; Inverse kinematics; Robot kinematics; Computer vision; Mathematics; Geometry; Artificial intelligence; Mobile robot; Theoretical computer science; Mathematical analysis; Physics; Classical mechanics","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":[0.001756651,0.0008592518,0.0008871813,0.0007583628,0.0006962628,0.001285474,0.001293748,0.001270658,0.002582265],"category_scores_gemma":[0.006001798,0.0005194556,0.001094521,0.000852282,0.003976589,0.004950461,0.003139178,0.001557366,0.0003248487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007920724,"about_ca_system_score_gemma":0.001591086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002651471,"about_ca_topic_score_gemma":0.003626812,"domain_scores_codex":[0.9987862,0.0003607639,0.0001105874,0.0002414476,0.0003640938,0.000136891],"domain_scores_gemma":[0.9960884,0.002873616,0.0003427608,0.0003319483,0.0002496073,0.0001136982],"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":[0.0001796263,0.00007965829,0.0007773866,0.0004500408,0.000041601,0.0003457405,0.00076128,0.4716296,0.006766317,0.4655482,0.001720741,0.05169988],"study_design_scores_gemma":[0.00004091575,0.000167646,0.0002917656,0.00006911963,0.00001680828,0.0001657993,0.0001656955,0.691691,0.005280654,0.2986892,0.003389554,0.00003176689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01068134,0.0001217796,0.9861865,0.0002025589,0.00001036716,0.00003779604,0.00005940036,0.0002202941,0.002480002],"genre_scores_gemma":[0.3719237,0.0005965804,0.6236505,0.0002046277,0.0000346925,0.000301737,0.0003476852,0.0001316265,0.002808851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002651471,"threshold_uncertainty_score":0.009290159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03259306086732413,"score_gpt":0.2617306445882835,"score_spread":0.2291375837209594,"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."}}