  The problem of mixed static and dynamic obstacle avoidance is essential for
path planning in highly dynamic environment. However, the paths formed by grid
edges can be longer than the true shortest paths in the terrain since their
headings are artificially constrained. Existing methods can hardly deal with
dynamic obstacles. To address this problem, we propose a new algorithm
combining Model Predictive Control (MPC) with Deep Deterministic Policy
Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory
of dynamic obstacles. Secondly, the DDPG with continuous action space is
designed to provide learning and autonomous decision-making capability for
robots. Finally, we introduce the idea of the Artificial Potential Field to set
the reward function to improve convergence speed and accuracy. We employ Unity
3D to perform simulation experiments in highly uncertain environment such as
aircraft carrier decks and squares. The results show that our method has made
great improvement on accuracy by 7%-30% compared with the other methods, and on
the length of the path and turning angle by reducing 100 units and 400-450
degrees compared with DQN (Deep Q Network), respectively.
