The application of path planning algorithm based on deep reinforcement learning for mobile robots

Tian, Siyi; Lei, Shuo; Huang, Qiming; Huang, Anyi

doi:10.1109/cost57098.2022.00084

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…In [54,100], a DQN network is established for the approximation of the values of the state and the action of a mobile robot. The system is composed of three phases: initially, the acquisition and processing of the image are carried out, for which two convolutional layers are defined, then the model is programmed to maximize the Q value in each training, and finally the DQN network selects the best possible action using two fully connected layers.…”

Section: Discussionmentioning

confidence: 99%

Autonomous Navigation of Robots: Optimization with DQN

et al. 2023

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In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning. However, previous studies have primarily focused on predefined paths, neglecting real-time obstacle avoidance and trajectory reconfiguration. This research introduces a novel algorithm that integrates reinforcement learning with the Deep Q-Network (DQN) to empower an agent with the ability to execute actions, gather information from a simulated environment in Gazebo, and maximize rewards. Through a series of carefully designed experiments, the algorithm’s parameters were meticulously configured, and its performance was rigorously validated. Unlike conventional navigation systems, our approach embraces the exploration of the environment, facilitating effective trajectory planning based on acquired knowledge. By leveraging randomized training conditions within a simulated environment, the DQN network exhibits superior capabilities in computing complex functions compared to traditional methods. This breakthrough underscores the potential of our algorithm to significantly enhance the autonomous learning capacities of mobile robots.

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Section: Discussionmentioning

confidence: 99%

Autonomous Navigation of Robots: Optimization with DQN

et al. 2023

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“…The incorporation of AI and DRL has introduced several advantages, including the automation of path planning, the establishment of interconnected cyber-physical systems, and the continuous exchange of real-time data facilitated by cloud computing. This integration has led to the development of machine learning-based control algorithms, elevating the capabilities of mobile robot path planning in diverse applications such as manufacturing processes [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm

Deguale,

Yu,

Sinishaw

et al. 2024

Sensors

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Path planning for mobile robots in complex circumstances is still a challenging issue. This work introduces an improved deep reinforcement learning strategy for robot navigation that combines dueling architecture, Prioritized Experience Replay, and shaped Rewards. In a grid world and two Gazebo simulation environments with static and dynamic obstacles, the Dueling Deep Q-Network with Modified Rewards and Prioritized Experience Replay (PMR-Dueling DQN) algorithm is compared against Q-learning, DQN, and DDQN in terms of path optimality, collision avoidance, and learning speed. To encourage the best routes, the shaped Reward function takes into account target direction, obstacle avoidance, and distance. Prioritized replay concentrates training on important events while a dueling architecture separates value and advantage learning. The results show that the PMR-Dueling DQN has greatly increased convergence speed, stability, and overall performance across conditions. In both grid world and Gazebo environments the PMR-Dueling DQN achieved higher cumulative rewards. The combination of deep reinforcement learning with reward design, network architecture, and experience replay enables the PMR-Dueling DQN to surpass traditional approaches for robot path planning in complex environments.

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The application of path planning algorithm based on deep reinforcement learning for mobile robots

Cited by 2 publications

References 13 publications

Autonomous Navigation of Robots: Optimization with DQN

Autonomous Navigation of Robots: Optimization with DQN

Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm

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