{"id":"W2156373593","doi":"10.1109/tsmcb.2006.877792","title":"Rendezvous-Guidance Trajectory Planning for Robotic Dynamic Obstacle Avoidance and Interception","year":2006,"lang":"en","type":"letter","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Rendezvous; Interception; Obstacle avoidance; Obstacle; Collision avoidance; Computer science; Trajectory; Position (finance); Robot; Motion planning; Control theory (sociology); Path (computing); Simulation; Artificial intelligence; Collision; Mobile robot; Engineering; Control (management); Aerospace engineering; Geography; Physics","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":[0.0003336883,0.0003201472,0.0002857077,0.0002963657,0.0003527635,0.0004627383,0.00061437,0.0009689544,0.003719528],"category_scores_gemma":[0.001549555,0.0001548655,0.0001480111,0.0003761342,0.0006477361,0.0006688343,0.0004679082,0.001103859,0.002531155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007603528,"about_ca_system_score_gemma":0.0003890323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005497838,"about_ca_topic_score_gemma":0.001147786,"domain_scores_codex":[0.9994372,0.0001626559,0.0000202083,0.00005634054,0.0002975782,0.00002601737],"domain_scores_gemma":[0.9995129,0.000220742,0.00004031685,0.00009968758,0.0001095848,0.00001676389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002873286,0.00004248865,0.0004860867,0.0004260986,0.00002598818,0.0009136087,0.0001491079,0.01746496,0.0191856,0.1373788,0.05536886,0.7682712],"study_design_scores_gemma":[0.00016509,0.0003586549,0.0008097955,0.0001516281,0.00002977916,0.005218841,0.00009049818,0.3586225,0.0268923,0.08507541,0.5225201,0.0000653036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006949496,0.008283299,0.9499166,0.006726553,0.001183114,0.00009647207,0.00004583142,0.001071976,0.02572659],"genre_scores_gemma":[0.3020871,0.01076735,0.628655,0.003670751,0.002185394,0.0004766424,0.0002370072,0.0002966601,0.05162408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003719528,"threshold_uncertainty_score":0.01244313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02571331981470601,"score_gpt":0.2528206833873409,"score_spread":0.2271073635726349,"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."}}