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According to the constraint relationship of the end\u2010effector trajectory of the dual\u2010arm coordinated transportation, the joint space trajectory mathematical model of the dual\u2010arm coordinated transportation was established by using the master\u2010slave construction method. Based on the time impact optimization index of joint trajectory, a multiobjective nonlinear equation is established. Using random probability distribution to extract the interpolation features of nonuniform quintic B\u2010spline trajectory, the feature optimization target is selected, and the Newton numerical algorithm is used for iterative optimization. At the same time, it is combined with an elite retention genetic algorithm to further optimize the target. Based on the disturbance and tracking problem, a PD control method based on time\u2010varying constrained output state is proposed, and the control law is designed. Its convergence is verified by establishing the Lyapunov function equation and asymmetric term. The trajectory optimization results show that the proposed trajectory optimization method can increase the individual diversity and enhance the individual local optimization, thus avoiding the premature impact of the elite retention genetic algorithm. Finally, the proposed control method is simulated on the platform of Gazebo; compared with the traditional PD control method, the results show that the proposed control algorithm has high robustness, and the rationality of the coordinated trajectory control method is verified by the double\u2010arm handling experiment.<\/jats:p>","DOI":"10.1155\/2021\/5338134","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T18:41:38Z","timestamp":1629139298000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time\u2010Varying Constrained Output State"],"prefix":"10.1155","volume":"2021","author":[{"given":"Hui","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Wenbin","family":"Zha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6055-6797","authenticated-orcid":false,"given":"Xiangrong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yongfei","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574718001339"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2020.00082"},{"key":"e_1_2_11_3_2","doi-asserted-by":"crossref","unstructured":"TellaecheA.andMaurtuaI. 6DOF pose estimation of objects for robotic manipulation. 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