Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning

Joint Authors

Lei, Xiaoyun
Zhang, Zhian
Dong, Peifang

Source

Journal of Robotics

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-09-18

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Mechanical Engineering

Abstract EN

Dynamic path planning of unknown environment has always been a challenge for mobile robots.

In this paper, we apply double Q-network (DDQN) deep reinforcement learning proposed by DeepMind in 2016 to dynamic path planning of unknown environment.

The reward and punishment function and the training method are designed for the instability of the training stage and the sparsity of the environment state space.

In different training stages, we dynamically adjust the starting position and target position.

With the updating of neural network and the increase of greedy rule probability, the local space searched by agent is expanded.

Pygame module in PYTHON is used to establish dynamic environments.

Considering lidar signal and local target position as the inputs, convolutional neural networks (CNNs) are used to generalize the environmental state.

Q-learning algorithm enhances the ability of the dynamic obstacle avoidance and local planning of the agents in environment.

The results show that, after training in different dynamic environments and testing in a new environment, the agent is able to reach the local target position successfully in unknown dynamic environment.

American Psychological Association (APA)

Lei, Xiaoyun& Zhang, Zhian& Dong, Peifang. 2018. Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning. Journal of Robotics،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1197833

Modern Language Association (MLA)

Lei, Xiaoyun…[et al.]. Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning. Journal of Robotics No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1197833

American Medical Association (AMA)

Lei, Xiaoyun& Zhang, Zhian& Dong, Peifang. Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning. Journal of Robotics. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1197833

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1197833