A review of motion planning algorithms for intelligent robotics

02/04/2021
by   Chengmin Zhou, et al.
63

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.

READ FULL TEXT

page 2

page 7

page 14

page 16

research
02/05/2021

An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios

Intelligent robots provide a new insight into efficiency improvement in ...
research
11/05/2016

Combining policy gradient and Q-learning

Policy gradient is an efficient technique for improving a policy in a re...
research
09/07/2023

Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning

Motion planning is the soul of robot decision making. Classical planning...
research
07/16/2023

Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review

Bayesian inference has many advantages in robotic motion planning over f...
research
04/15/2019

A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms

Consistently checking the statistical significance of experimental resul...
research
12/18/2022

Risk-Sensitive Reinforcement Learning with Exponential Criteria

While risk-neutral reinforcement learning has shown experimental success...
research
09/17/2021

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

The challenge of mapping indoor environments is addressed. Typical heuri...

Please sign up or login with your details

Forgot password? Click here to reset