{"id":"W4252671680","doi":"10.22215/etd/2015-11093","title":"Evolutionary Neural Network-Based Obstacle Avoidance for a Planetary Exploration Rover","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Traverse; Obstacle avoidance; Computer science; Motion planning; Artificial neural network; Path (computing); Mobile robot; Artificial intelligence; Set (abstract data type); Obstacle; Robot; Real-time computing; Geography; Computer network","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":[0.0001880101,0.0002883584,0.0003159031,0.0001730735,0.0002416481,0.0003484215,0.000590308,0.0005246501,0.001300078],"category_scores_gemma":[0.0005797982,0.000196346,0.0002555636,0.0001932806,0.0002590007,0.000277441,0.0004253504,0.0006672763,0.0001403331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004160796,"about_ca_system_score_gemma":0.000371569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007126019,"about_ca_topic_score_gemma":0.006342987,"domain_scores_codex":[0.9999458,0.000009126494,0.000002686318,0.00001560407,0.00001413191,0.00001263345],"domain_scores_gemma":[0.9999237,0.00003226295,0.000008318732,0.00000399339,0.00002418493,0.000007480483],"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":[0.00002111752,0.00002225798,0.0002958053,0.00001226873,0.00001604374,0.00003649517,0.00002238005,0.9639585,0.001800721,0.002138471,0.0004165305,0.03125939],"study_design_scores_gemma":[0.00000187319,0.000006979152,0.00006488695,0.000001432829,0.000001807855,0.000002805043,0.000002016355,0.9992263,0.0001252707,0.0004485974,0.000116947,0.000001089551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1843417,0.0010938,0.7998493,0.0007499623,0.0001381268,0.00005346682,0.0000559654,0.0003784749,0.01333922],"genre_scores_gemma":[0.9304359,0.0002982694,0.06146527,0.000116045,0.00003173417,0.00006571923,0.00006884355,0.00003073898,0.007487571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007126019,"threshold_uncertainty_score":0.0141691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04149337174791654,"score_gpt":0.2838312056857438,"score_spread":0.2423378339378272,"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."}}