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A review of motion planning algorithms for intelligent robotics

arXiv 2102.02376 ↗First posted 4 Feb 2021cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 84 references that are in the map. First posted on arXiv 4 Feb 2021.

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References · 24

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12 more
  1. 2016Safe and Efficient Off-Policy Reinforcement Learningcited by 260
  2. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 162
  3. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 104
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  12. 2019Harnessing Reinforcement Learning for Neural Motion Planningcited by 19

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