{"id":"https://openalex.org/W7204276366","doi":"https://doi.org/10.69626/cai.2025.0205","title":"Improving Robot Performance through Machine Learning\u2013Based Battery Voltage Estimation","display_name":"Improving Robot Performance through Machine Learning\u2013Based Battery Voltage Estimation","publication_year":2025,"publication_date":"2025-06-27","ids":{"openalex":"https://openalex.org/W7204276366","doi":"https://doi.org/10.69626/cai.2025.0205"},"language":null,"primary_location":{"id":"doi:10.69626/cai.2025.0205","doi":"https://doi.org/10.69626/cai.2025.0205","is_oa":true,"landing_page_url":"https://doi.org/10.69626/cai.2025.0205","pdf_url":"https://backend.aristonpubs.com/article/article-1766466247577.pdf","source":{"id":"https://openalex.org/S5407051827","display_name":"CompSci & AI Advances","issn_l":"3066-3806","issn":["3066-3806"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CompSci & AI Advances","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://backend.aristonpubs.com/article/article-1766466247577.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101471859","display_name":"Abdul Samad","orcid":"https://orcid.org/0000-0003-0798-2951","observed_orcids":["https://orcid.org/0000-0003-0798-2951"]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abdul Samad","raw_affiliation_strings":["Vellore Institute of Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology University","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5148318515","display_name":"Mohammad Jainul","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mohammad Jainul","raw_affiliation_strings":["Vellore Institute of Technology University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vellore Institute of Technology University","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.47175067,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"02","issue":"03","first_page":"205","last_page":"205"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13382","display_name":"Robotics and Automated Systems","score":0.70169997215271,"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"}},"topics":[{"id":"https://openalex.org/T13382","display_name":"Robotics and Automated Systems","score":0.70169997215271,"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"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0357000008225441,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10571","display_name":"Robotic Mechanisms and Dynamics","score":0.019300000742077827,"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/joint-angles","display_name":"joint angles","score":0.4869999885559082},{"id":"https://openalex.org/keywords/motor-torque","display_name":"motor torque","score":0.9980000257492065},{"id":"https://openalex.org/keywords/sensor-latency","display_name":"sensor latency","score":0.9959999918937683},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7490000128746033},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.7699999809265137},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.9900000095367432},{"id":"https://openalex.org/keywords/regression-trees","display_name":"regression trees","score":0.968999981880188},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.9599999785423279},{"id":"https://openalex.org/keywords/predictive-maintenance","display_name":"predictive maintenance","score":0.6980000138282776},{"id":"https://openalex.org/keywords/power-efficiency","display_name":"power efficiency","score":0.47699999809265137}],"concepts":[{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7258999943733215},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5611000061035156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5562999844551086},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.503600001335144},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.498199999332428},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3765000104904175},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.3366999924182892},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C144171764","wikidata":"https://www.wikidata.org/wiki/Q48103","display_name":"Torque","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C2776266606","wikidata":"https://www.wikidata.org/wiki/Q476482","display_name":"Test bench","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.69626/cai.2025.0205","doi":"https://doi.org/10.69626/cai.2025.0205","is_oa":true,"landing_page_url":"https://doi.org/10.69626/cai.2025.0205","pdf_url":"https://backend.aristonpubs.com/article/article-1766466247577.pdf","source":{"id":"https://openalex.org/S5407051827","display_name":"CompSci & AI Advances","issn_l":"3066-3806","issn":["3066-3806"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CompSci & AI Advances","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.69626/cai.2025.0205","doi":"https://doi.org/10.69626/cai.2025.0205","is_oa":true,"landing_page_url":"https://doi.org/10.69626/cai.2025.0205","pdf_url":"https://backend.aristonpubs.com/article/article-1766466247577.pdf","source":{"id":"https://openalex.org/S5407051827","display_name":"CompSci & AI Advances","issn_l":"3066-3806","issn":["3066-3806"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CompSci & AI Advances","raw_type":"journal-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204276366.pdf","grobid_xml":"https://content.openalex.org/works/W7204276366.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,205],"research":[1],"explores":[2,23],"the":[3,24,115,124,133,144,157,161,186,208],"intersection":[4],"of":[5,26,61,211],"artificial":[6],"intelligence":[7],"and":[8,36,56,69,75,86,94,156,225,245,254],"robotics,":[9],"focusing":[10],"on":[11,44,123,132,160,170,189],"integrating":[12],"machine":[13],"learning":[14],"for":[15,250],"predictive":[16,252],"modeling":[17],"in":[18,215,257],"robotic":[19,216],"systems.":[20],"Specifically,":[21],"it":[22],"use":[25],"four":[27],"regression":[28,119],"algorithms,":[29],"decision":[30,187],"tree,":[31],"random":[32],"forest,":[33],"Ada":[34],"boost,":[35,38],"XG":[37,183],"to":[39,66],"predict":[40],"battery":[41,105,212],"voltage":[42,106,213],"based":[43],"key":[45],"operational":[46],"parameters":[47],"such":[48],"as":[49,218],"joint":[50,90],"angles,":[51],"motor":[52,87],"torque,":[53],"sensor":[54,95],"latency,":[55],"end-of-performance":[57,84],"error.":[58],"A":[59],"dataset":[60],"20":[62],"observations":[63],"was":[64],"used":[65],"analyze":[67],"performance":[68,109],"find":[70],"relationships":[71],"between":[72,83,243],"mechanical":[73],"factors":[74],"power":[76,219],"efficiency.":[77],"The":[78,108,229],"analysis":[79],"found":[80],"strong":[81,101,150],"correlations":[82],"error":[85],"torque":[88],"(0.97),":[89],"angle":[91],"1":[92],"(0.93),":[93],"latency":[96],"(0.89),":[97],"while":[98],"showing":[99],"a":[100,149,239],"negative":[102],"correlation":[103],"with":[104,148,197],"(-0.95).":[107],"evaluation":[110],"revealed":[111],"distinct":[112],"patterns":[113],"across":[114],"models.":[116],"Decision":[117],"tree":[118,188],"achieved":[120],"perfect":[121],"accuracy":[122,179,191,224,244],"training":[125,151,171,190],"data":[126,135,163],"(R\u00b2":[127,136,153,164,172,180,192,199],"=":[128,137,154,165,173,181,193,200],"1.0000)":[129,194],"but":[130,175,195],"underperformed":[131],"test":[134,162,178],"0.8040),":[138],"indicating":[139,202],"overfitting.":[140,204],"Random":[141,236],"Forest":[142],"provided":[143],"best":[145],"overall":[146,226],"performance,":[147],"score":[152],"0.9742)":[155],"highest":[158],"generalization":[159,198],"0.8553).":[166],"AdaBoost":[167],"performed":[168],"well":[169],"0.9986)":[174],"showed":[176],"moderate":[177],"0.7666).":[182],"Boost":[184],"matched":[185],"struggled":[196],"0.6570),":[201],"significant":[203],"study":[206],"underscores":[207],"critical":[209],"role":[210],"monitoring":[214],"systems,":[217],"fluctuations":[220],"directly":[221],"affect":[222],"position":[223],"system":[227,255],"reliability.":[228],"findings":[230],"suggest":[231],"that":[232],"ensemble":[233],"models,":[234],"especially":[235],"Forest,":[237],"offer":[238],"more":[240],"stable":[241],"balance":[242],"generalization,":[246],"making":[247],"them":[248],"suitable":[249],"real-time":[251],"maintenance":[253],"optimization":[256],"robotics.":[258]},"counts_by_year":[],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2026-08-27T00:00:00"}