Vibration has a great influence on the working accuracy of flexible-link manipulators. In this paper, an online trajectory planning method for the flexible-link manipulator is proposed to suppress the vibration. Firstly, the vibration dynamic model of planar flexible-link manipulator is established, and the actual vibration trajectory of manipulator is solved by reasonable simplification. Then, taking residual energy of vibration as an objective function, the optimizer based on Particle Swarm Optimization(PSO) intelligent search algorithm is employed to search the motion trajectory of the manipulator with the best vibration suppression effect. Finally, the optimizer based on back propagation neural networks(BPNN) is employed instead of PSO optimizer to generate manipulator motion trajectory rapidly, and the optimal trajectory database searched by PSO optimizer is used to train BPNN. Simulation results show that this method can generate the trajectory rapidly and suppress the vibration effectively, and can be applied to the online trajectory planning of flexible manipulator.

Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.