Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motionplanning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actorcritic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.

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This paper deals with the design, dynamic modelling and sliding mode control of multiple cooperative welding robot manipulators (MWRMs). The MWRMs can handle complex tasks that are difficult or even impossible for a single manipulator. The kinematics and dynamics of the MWRMs are studied on the basis of the Denavit‐Hartenberg and Lagrange method. Following that, considering the MWRM system with nonlinear and unknown disturbances, a non‐singular terminal sliding mode control strategy is designed. By means of the Lyapunov function, the stability of the controller is proved. Simulation results indicate that the good control performance of the MWRMs is achieved by the non‐singular terminal sliding mode controller, which also illustrates the correctness of the dynamic modelling and effectiveness of the proposed control strategy

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