Deep Deterministic Policy Gradient for Navigation of Mobile Robots

Jesus, Junior Costa de; Bottega, Jair Augusto; Cuadros, Marco Antônio de Souza Leite; Gamarra, Daniel Fernando Tello

doi:10.3233/jifs-191711

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…The difference in the convergence effect between the three algorithms of DDPG (proposed in Ref. [21]), LSTM‐DDPG (proposed in Ref. [22]), and APF‐LSTM DDPG is slight.…”

Section: Simulation Experiments Resultsmentioning

confidence: 99%

A path planning algorithm fusion of obstacle avoidance and memory functions

Zheng,

Li,

Zhu

et al. 2023

Cognitive Comp and Systems

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In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions.

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“…The difference in the convergence effect between the three algorithms of DDPG (proposed in Ref. [21]), LSTM‐DDPG (proposed in Ref. [22]), and APF‐LSTM DDPG is slight.…”

Section: Simulation Experiments Resultsmentioning

confidence: 99%

A path planning algorithm fusion of obstacle avoidance and memory functions

Zheng,

Li,

Zhu

et al. 2023

Cognitive Comp and Systems

View full text Add to dashboard Cite

show abstract

“…For example, in refs. [12][13][14], reinforcement learning (RL) agents are able to generate an optimum path for trajectory tracking by utilizing grid world simulation. Furthermore, RL agents are known to be able to produce control inputs based on sophisticated data without explicitly telling its correlation.…”

Section: Introductionmentioning

confidence: 99%

“…However, the works in refs. [12][13][14][15][16][17][18][19][20][21][22][23][24] do not involve the dynamics of the DDMR, which results in indirect control inputs using turning directions or velocities.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, unlike previous studies where the linear and angular velocities outputs were closely proportional to the distance and heading control feedback (as evident in refs. [12][13][14][15][16][17][18][19][20][21][22][23][24]), this study challenges the RL agent to produce two continuous voltages simultaneously for trajectory tracking of the DDMR.…”

Section: Introductionmentioning

confidence: 99%

“…This methodology constitutes an innovative contribution that deviates from previous works in refs. [12][13][14][15][16][17][18][19][20][21][22][23][24]. In addition, unlike prior works in refs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Zaman,

2024

Robotica

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In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

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Model free position-force control of environmental constrained reconfigurable manipulators based on adaptive dynamic programming

Ma,

Yao,

et al. 2023

Artif Intell Rev

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Deep Deterministic Policy Gradient for Navigation of Mobile Robots

Cited by 7 publications

References 4 publications

A path planning algorithm fusion of obstacle avoidance and memory functions

A path planning algorithm fusion of obstacle avoidance and memory functions

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Model free position-force control of environmental constrained reconfigurable manipulators based on adaptive dynamic programming

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