Journal of System Simulation
Abstract
Abstract: In order to solve the path planning problem of mobile robots in dynamic environment, two-layer path planning algorithm based on improved ant colony algorithm and MA-DQN algorithm is proposed. Static global path planning is accomplished by ant colony algorithm that improved the probabilistic transfer function and the pheromone updating principle; the traditional DQN algorithm structure is improved by using the memristor as the synaptic structure of neural network, and then completed the local dynamic obstacle avoidance of the mobile robot. The path planning mechanism is switched according to whether there are dynamic obstacles within the sensing range of the mobile robot, so as to completed the path planning task in the dynamic environment. The simulation results show that the algorithm can effectively plan a feasible path for mobile robots in a dynamic environment in real time.
Recommended Citation
Yang, Hailan; Qi, Yongqiang; Wu, Baolei; and Rong, Dan
(2023)
"Path Planning of Mobile Robots Based on Memristor Reinforcement
Learning in Dynamic Environment,"
Journal of System Simulation: Vol. 35:
Iss.
7, Article 18.
DOI: 10.16182/j.issn1004731x.joss.22-0334
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss7/18
First Page
1619
Last Page
1633
CLC
TP242; TP391
Recommended Citation
Yang Hailan, Qi Yongqiang, Wu Baolei, et al. Path Planning of Mobile Robots Based on Memristor Reinforcement Learning in Dynamic Environment[J]. Journal of System Simulation, 2023, 35(7): 1619-1633.
DOI
10.16182/j.issn1004731x.joss.22-0334
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