Aiming at the problems of complex trajectory learning and lack of coordination constraint analysis when a dual-robot collaborative system performs humanoid tasks with strong coordination constraints, this paper proposed a dual-robot cooperative handling trajectory learning and generalization method based on dynamic movement primitives (DMPs). Firstly, starting from the dual-robot cooperative handling task, the coordination constraints of the dual-robot were analyzed, and the motion constraint model of the dual-robot was established. Then, the robot motion trajectory was decoupled into position trajectory and orientation trajectory, and the quaternion was used to realize the non-singular description of the orientation trajectory. And the dynamic movement primitives model of position trajectory and orientation trajectory were established respectively. They were combined with the dual robot motion constraint model and DMPs model, and the dual-robot movement trajectory was obtained, taking into account their respective task requirements and relative pose constraints. Finally, the simulation and experiments of the cooperative handling trajectory of the two robots were carried out. The results show that: using the learning and generalization method of the dual-robot cooperative handling trajectory, when the starting and ending states are changed, the position errors of start point and end point of the dual-robot cooperative handling with the fixed orientation are 0.0292 mm and 0.1127 mm respectively; the position errors of start point and end point of variable orientation coordinated handling are 0.0323 mm and 0.1131 mm respectively; and the quaternion orientation errors of the end point are 0.0014, 0.0027, 0.0018, 0.0030, indicating that the cooperative handling trajectory learning and generalization method has high motion control accuracy; even if the task parameters of the starting and ending are changed, the generalized trajectory can still ensure the accessibility of the target, which verified the scientificity and effectiveness of the proposed dual-robot coordination motion trajectory control strategy. The method proposed in this paper can effectively learn the human handling process and can accurately generalize new motion trajectories. It realizes the dual-robot coordinated motion and has important engineering application value.

Heavy quadruped robots are subjected to uncertain impact loads during foot-to-ground contact and gait transition, which can easily lead to excessive load on the foot mechanism and structural impact damage. Therefore, a sliding mode impedance control method based on environmental parameter estimation (EPESM) was proposed to solve the problem of poor dynamic performance when using hydraulic series elastic actuators (SEA) as foot ends in unstructured environments. Based on the piston displacement transfer function of the valve controlled hydraulic cylinder, an SEA impedance control model based on the position inner loop is established, with PID serving as the basic controller. To improve the dynamic performance of SEA impedance control, a stable adaptive environment parameter estimation method based on Lyapunov ’s second method is constructed to compensate for the expected SEA position using feed forward compensation. To improve the dynamic performance and adaptability of adaptive environmental parameter estimation methods at different stages of SEA work, fuzzy control methods are used to optimize the adaptive parameters in these methods. Based on the SEA state equation, a sliding mode controller and a PID controller are constructed for dynamic performance comparison and analysis. Simulation results show that under variable SEA spring stiffness and variable ambient stiffness conditions, the response speed of EPESM impedance control is significantly faster. The adjustment time can be significantly reduced from an average of 5 s to within 1 s, achieving faster expected displacement and expected contact force, while keeping the steady-state error slightly reduced and the contact force error within ±6 N. Under dynamic tracking conditions, EPESM impedance control exhibits better dynamic performance, maintaining a phase delay within 0.2 s and an amplitude error of 5.2 % for an extended period after quickly entering the tracking state.