{"id":"https://openalex.org/W4200540936","doi":"https://doi.org/10.32604/cmc.2022.022183","title":"Path Planning Based on the Improved RRT* Algorithm for the Mining Truck","display_name":"Path Planning Based on the Improved RRT* Algorithm for the Mining Truck","publication_year":2021,"publication_date":"2021-12-07","ids":{"openalex":"https://openalex.org/W4200540936","doi":"https://doi.org/10.32604/cmc.2022.022183"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2022.022183","doi":"https://doi.org/10.32604/cmc.2022.022183","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2022.022183","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2022.022183","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100391554","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0003-3627-2051","observed_orcids":["https://orcid.org/0000-0003-3627-2051"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dong Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074820600","display_name":"Shutong Zheng","orcid":"https://orcid.org/0009-0004-0451-4305","observed_orcids":["https://orcid.org/0009-0004-0451-4305"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shutong Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027564334","display_name":"Yanxi Ren","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanxi Ren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5057213664","display_name":"Danjie Du","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Danjie Du","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5100391554"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.44,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.81975309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"71","issue":"2","first_page":"3571","last_page":"3587"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13065","display_name":"Mining Techniques and Economics","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/path-planning","display_name":"path planning","score":0.8669999837875366},{"id":"https://openalex.org/keywords/rrt","display_name":"RRT*","score":0.8790000081062317},{"id":"https://openalex.org/keywords/mining-trucks","display_name":"mining trucks","score":0.8460000157356262},{"id":"https://openalex.org/keywords/bidirectional-rrt","display_name":"bidirectional RRT","score":0.9559999704360962},{"id":"https://openalex.org/keywords/dubins-curves","display_name":"Dubins curves","score":0.9449999928474426},{"id":"https://openalex.org/keywords/variable-step-size","display_name":"variable step size","score":0.7020000219345093},{"id":"https://openalex.org/keywords/obstacle-detection","display_name":"obstacle detection","score":0.6119999885559082},{"id":"https://openalex.org/keywords/path-smoothing","display_name":"path smoothing","score":0.8489999771118164},{"id":"https://openalex.org/keywords/cubic-b-spline-interpolation","display_name":"cubic B-spline interpolation","score":0.9300000071525574},{"id":"https://openalex.org/keywords/ackermann-steering","display_name":"Ackermann steering","score":0.7210000157356262},{"id":"https://openalex.org/keywords/mining-efficiency","display_name":"mining efficiency","score":0.38199999928474426}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6831170916557312},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.639596700668335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5302873253822327},{"id":"https://openalex.org/C2776839635","wikidata":"https://www.wikidata.org/wiki/Q14942679","display_name":"Random tree","level":4,"score":0.5236996412277222},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.48130717873573303},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4562581181526184},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4404742121696472},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3006128668785095},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.17649978399276733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13591942191123962},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2022.022183","doi":"https://doi.org/10.32604/cmc.2022.022183","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2022.022183","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2022.022183","doi":"https://doi.org/10.32604/cmc.2022.022183","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2022.022183","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5378999710083008}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1971998222","https://openalex.org/W2046456604","https://openalex.org/W2078505369","https://openalex.org/W2086860466","https://openalex.org/W2117748184","https://openalex.org/W2535022623","https://openalex.org/W2777447168","https://openalex.org/W2785456474","https://openalex.org/W2787245337","https://openalex.org/W2891447548","https://openalex.org/W2901884405","https://openalex.org/W2947931875","https://openalex.org/W2952077217","https://openalex.org/W3002939557","https://openalex.org/W3003202703","https://openalex.org/W3034808441","https://openalex.org/W3080880247","https://openalex.org/W3107417205"],"related_works":["https://openalex.org/W4324118986","https://openalex.org/W2785456474","https://openalex.org/W7084229175","https://openalex.org/W4206617872","https://openalex.org/W4392878903","https://openalex.org/W4308805336","https://openalex.org/W7124162599","https://openalex.org/W4413655379","https://openalex.org/W4319978505","https://openalex.org/W4205962758"],"abstract_inverted_index":{"Planning":[0],"a":[1,11,21,64],"reasonable":[2],"driving":[3],"path":[4,22,179],"for":[5,168,178],"trucks":[6],"in":[7,40,152,206,216],"mining":[8,16,148],"areas":[9],"is":[10,32,51,61,67,82,102,109,158,176],"key":[12],"point":[13,81],"to":[14,69,79,111,137,184],"improve":[15],"efficiency.":[17],"In":[18,94,135,170],"this":[19],"paper,":[20],"planning":[23],"method":[24,89],"based":[25,160],"on":[26,161],"Rapidly-exploring":[27],"Random":[28],"Tree":[29],"Star":[30],"(RRT*)":[31],"proposed,":[33],"and":[34,63,90,104,131,141,166,181,210],"several":[35],"optimizations":[36],"are":[37,124],"carried":[38],"out":[39],"the":[41,44,54,58,73,85,95,105,114,117,121,128,139,144,153,162,171,173,186,191,194,199,207,212],"algorithm.":[42,118],"Firstly,":[43],"selection":[45],"process":[46,55,75],"of":[47,56,116,143,155,190],"growth":[48],"target":[49],"points":[50],"optimized.":[52],"Secondly,":[53],"selecting":[57],"parent":[59],"node":[60,78],"optimized":[62,83],"Dubins":[65,99],"curve":[66,100,183],"used":[68,177],"constraint":[70,101],"it.":[71],"Then,":[72],"expansion":[74],"from":[76],"tree":[77],"random":[80],"by":[84,126],"gravitational":[86],"repulsion":[87],"field":[88],"dynamic":[91],"step":[92],"method.":[93],"obstacle":[96],"detection":[97],"process,":[98],"used,":[103],"bidirectional":[106,201],"RRT*":[107,202,214],"algorithm":[108,130,175,203,215],"adopted":[110],"speed":[112],"up":[113],"iteration":[115],"After":[119],"that,":[120],"obtained":[122],"paths":[123],"smoothed":[125],"using":[127],"greedy":[129],"cubic":[132],"B-spline":[133],"interpolation.":[134],"addition,":[136],"verify":[138],"superiority":[140],"correctness":[142],"algorithm,":[145],"an":[146],"unmanned":[147],"vehicle":[149],"kinematic":[150],"model":[151],"form":[154],"front-wheel":[156],"steering":[157,164],"developed":[159],"Ackermann":[163],"principle":[165],"simulated":[167],"CoppeliaSim.":[169],"simulation,":[172],"Stanley":[174],"tracking":[180],"Reeds-Shepp":[182],"adjust":[185],"final":[187],"parking":[188],"attitude":[189],"truck.":[192],"Finally,":[193],"experimental":[195],"comparison":[196],"shows":[197],"that":[198],"improved":[200],"performs":[204],"well":[205],"simulation":[208],"experiment,":[209],"outperforms":[211],"common":[213],"various":[217],"aspects.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-12-31T00:00:00"}