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For this purpose, a model is developed to quantify human performance considering the effects of physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload, workloads due to the robots\u2019 mistakes, and task complexity. The performance of multi-robot in the HRC setting is modeled based upon the rate of task assignment and completion as well as the mistake probabilities of the individual robots. The human trust in HRC setting with single and multiple robots is modeled over different operation regions, namely unpredictable region, predictable region, dependable region, and faithful region. The relative performance difference between the human operator and the robot is used to analyze the effect on the human operator\u2019s trust in robots\u2019 operation. The developed model is simulated for a manufacturing workspace scenario considering different task complexities and involving multiple robots to complete shared tasks. The simulation results indicate that for a constant multi-robot performance in operation, the human operator\u2019s trust in robots\u2019 operation improves whenever the comparative performance of the robots improves with respect to the human operator performance. The impact of robot hypothetical learning capabilities on human trust in the same HRC setting is also analyzed. The results confirm that a hypothetical learning capability allows robots to reduce human workloads, which improves human performance. The simulation result analysis confirms that the human operator\u2019s trust in the multi-robot operation increases faster with the improvement of the multi-robot performance when the robots have a hypothetical learning capability. An empirical study was conducted involving a human operator and two collaborator robots with two different performance levels in a software-based HRC setting. The experimental results closely followed the pattern of the developed mathematical models when capturing human trust and performance in terms of human\u2013multi-robot collaboration.<\/jats:p>","DOI":"10.1145\/3660648","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T19:51:06Z","timestamp":1713815466000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Performance-Aware Trust Modeling within a Human\u2013Multi-Robot Collaboration Setting"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3081-6388","authenticated-orcid":false,"given":"Md Khurram Monir","family":"Rabby","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, North Carolina A&amp;T State University, Greensboro, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6938-5525","authenticated-orcid":false,"given":"Mubbashar Altaf","family":"Khan","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, North Carolina A&amp;T State University, Greensboro, North Carolina, USA and Manufacturing Engineering Department, Central State University, Wilberforce, Ohio, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6937-927X","authenticated-orcid":false,"given":"Steven Xiaochun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Industrial and Systems Engineering Department, North Carolina A&amp;T State University, Greensboro, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6084-6831","authenticated-orcid":false,"given":"Ali","family":"Karimoddini","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, North Carolina A&amp;T State University, Greensboro, North Carolina, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3088082"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219843608001303"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319502.3374804"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/0-8176-4404-0_4"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2013.2293535"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2015.245"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359616"},{"key":"e_1_3_2_9_1","unstructured":"Houston Claure Yifang Chen Jignesh Modi Malte Jung and Stefanos Nikolaidis. 2019. 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