Efficiency can be improved through the cooperation of a dual-robot during assembly. However, how to effectively plan a simple and smooth path in a dynamic environment is a prominent problem in the process of dual-robot cooperative assembly. In this paper, a method based on RRT-Connect algorithm for trajectory planning and post-processing for trajectory optimization is proposed. This method takes full advantage of the excellent solution ability of RRT-Connect algorithm in the complex environment so as to obtain the initial path successfully. Through post-processing, the problem of RRT-Connect non-convergence to target is optimized. We use two 6-DOF industrial robots to build an experimental platform and design a dual-robot cooperative assembly system. According to the given task, the system can generate the original collision-free path based on RRT-Connect algorithm. Then the original path is simplified by Floyd algorithm and smoothed by multi-segment Bezier curve. Finally, the time parameter is sequenced for all the path points based on the iterative method, and the effective trajectory is obtained. The experimental results show that the algorithm proposed in this paper can effectively plan and optimize the trajectory of dual-robot. Compared to other methods, this approach has a higher success rate and less planning time.

The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study its instability, existing works mostly consider the uncommon double-loop variant or basic models with finite state and action space. We investigate the more practical single-sample two-timescale AC for solving the canonical linear quadratic regulator (LQR) problem, where the actor and the critic update only once with a single sample in each iteration on an unbounded continuous state and action space. Existing analysis cannot conclude the convergence for such a challenging case. We develop a new analysis framework that allows establishing the global convergence to an epsilon-optimal solution with at most an order of epsilon to -2.5 sample complexity. To our knowledge, this is the first finite-time convergence analysis for the single sample two-timescale AC for solving LQR with global optimality. The sample complexity improves those of other variants by orders, which sheds light on the practical wisdom of single sample algorithms. We also further validate our theoretical findings via comprehensive simulation comparisons.