This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight 2D environment. Then, we design the algorithm based on DRL, including observation states, reward function, network structure as well as parameters optimization, in a 2D environment to circumvent the time-consuming works for a 3D environment. We transfer the designed algorithm to a simple 3D environment for retraining to obtain the converged network parameters, including the weights and biases of deep neural network (DNN), etc. Using these parameters as initial values, we continue to train the model in a complex 3D environment. To improve the generalization of the model in different scenes, we propose to combine the DRL algorithm Twin Delayed Deep Deterministic policy gradients (TD3) with the traditional global path planning algorithm Probabilistic Roadmap (PRM) as a novel path planner (PRM+TD3). Experimental results show that the incremental training mode can notably improve the development efficiency. Moreover, the PRM+TD3 path planner can effectively improve the generalization of the model.

A ring-opening/alkyne–carbonyl metathesis sequence of alkyne-tethered cyclobutanones catalyzed by AgSbF6 is realized for the first time to furnish multisubstituted naphthyl ketones under mild conditions. A range of substrates decorated with various substituents at different positions were all well accommodated. Preliminary mechanistic studies show that silver salt acted as a Lewis acid to facilitate both C–C cleavage of the cyclobutanone moiety and the subsequent metathesis between CO and CC bonds.

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Abstract. In this paper, the sliding mode tracking control is proposed for a class of uncertain non-affine nonlinear systems via the nonlinear disturbance observer(NDOB). Based on the Taylor expansion method, the affine system is approximated to facilitate the desired control design. Then, the NDOB is adopted to estimate the unknown disturbance. Subsequently, based on approximated affine nonlinear model and the NDOB, the sliding mode tracking control is proposed for non-affine nonlinear systems with uncertainty. The control scheme can guarantee semi-global uniform boundedness of the closed-loop system signals as proved by Lyapunov analysis. Numerical simulation results are presented to illustrate the effectiveness of the proposed sliding mode tracking control scheme.