{"id":"W4360584313","doi":"10.1109/access.2023.3260646","title":"Toward Safer and Energy Efficient Global Trajectory Planning of Self-Guided Vehicles for Material Handling System in Dynamic Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Trajectory; Collision avoidance; Energy consumption; Motion planning; Real-time computing; Collision; Obstacle avoidance; Mobile robot; Obstacle; Kinematics; Simulation; Artificial intelligence; Robot; Engineering; 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":[0.0003342142,0.0008795027,0.0004182888,0.000560037,0.0004093975,0.0005529551,0.0007558866,0.0005387543,0.0009535022],"category_scores_gemma":[0.000765204,0.0003448977,0.0005399275,0.0004288911,0.0003625078,0.0005796672,0.0006934243,0.0006175796,0.0002244609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005771036,"about_ca_system_score_gemma":0.001647257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01388375,"about_ca_topic_score_gemma":0.01217841,"domain_scores_codex":[0.9998272,0.00003785059,0.000008536316,0.0000364593,0.00005701256,0.00003291946],"domain_scores_gemma":[0.9997423,0.000069108,0.00005023885,0.0000370111,0.00007775567,0.00002356004],"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":[0.00005151473,0.00002201072,0.0009594993,0.00004460529,0.00002700457,0.00006924261,0.000101469,0.94095,0.004564553,0.00304533,0.0005927949,0.04957188],"study_design_scores_gemma":[0.000003319444,0.00001785768,0.0001520471,0.000003154479,0.00000403374,0.00001175243,0.00002338129,0.9976604,0.00077924,0.0008461818,0.0004954507,0.000003178571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02001474,0.000176165,0.978231,0.00007190741,0.00001604092,0.00003262027,0.00003076631,0.0005193009,0.0009073735],"genre_scores_gemma":[0.7180144,0.000310669,0.2791312,0.00004699691,0.00001518211,0.0001360285,0.0002663421,0.0001058149,0.001973436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01388375,"threshold_uncertainty_score":0.02760583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03684971833524189,"score_gpt":0.2894456594687944,"score_spread":0.2525959411335524,"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."}}