  We investigate and analyze principles of typical motion planning algorithms.
These include traditional planning algorithms, supervised learning, optimal
value reinforcement learning, policy gradient reinforcement learning.
Traditional planning algorithms we investigated include graph search
algorithms, sampling-based algorithms, and interpolating curve algorithms.
Supervised learning algorithms include MSVM, LSTM, MCTS and CNN. Optimal value
reinforcement learning algorithms include Q learning, DQN, double DQN, dueling
DQN. Policy gradient algorithms include policy gradient method, actor-critic
algorithm, A3C, A2C, DPG, DDPG, TRPO and PPO. New general criteria are also
introduced to evaluate performance and application of motion planning
algorithms by analytical comparisons. Convergence speed and stability of
optimal value and policy gradient algorithms are specially analyzed. Future
directions are presented analytically according to principles and analytical
comparisons of motion planning algorithms. This paper provides researchers with
a clear and comprehensive understanding about advantages, disadvantages,
relationships, and future of motion planning algorithms in robotics, and paves
ways for better motion planning algorithms.
