A Hybrid Tracking Control Strategy for Nonholonomic Wheeled Mobile Robot Incorporating Deep Reinforcement Learning Approach

Gao, Xueshan; Gao, Rui; Liang, Ping; Zhang, Qingfang; Rui, Deng; Zhu, Wei

doi:10.1109/access.2021.3053396

Cited by 19 publications

(9 citation statements)

References 31 publications

(29 reference statements)

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“…Not all planning algorithms are appropriate for every situation and include many factors affected by the site. How to plan the safety process quickly and efficiently is the key to completing the project [3]. Currently, a lot of research has been done in the field of mobile robot planning at home and abroad, and good results have been achieved.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

Zhu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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In order to optimize robot road planning, automation will increase production efficiency and reduce production costs. An improved ant colony algorithm based on a two-dimensional flat path planning study divided the three-dimensional space into planes, rasterized each plane, and replaced the original shape by storing a pheromone at the intersection. The pheromone storage space along the route will be reduced, and a three-dimensional space route planning study will be gradually conducted. As complex 3D environments and landscape features change, obstacle avoidance strategies have increased, road heuristics have improved, and new heuristic features have emerged. The initial value of the road node pheromone increases the efficiency of early ant search because of the uneven distribution of starting point, target point location information, and forward direction. After each iteration, the stuck ants that did not reach the target point are discarded according to the high-quality ant renewal rules, the iteration threshold is set, and the pheromone fluctuation coefficient is adjusted as the algorithm tends to merge. Compared with the basic ant colony algorithm, the convergence iteration times of the improved ant colony algorithm in this paper are reduced by about 40%, and the optimal path length is shortened by about 10. The duration of the algorithm increases. In terms of algorithm performance, it takes some time to improve the ant colony algorithm. Because of the complexity of the algorithm, some search strategies are added to the algorithm. The contribution of this article is the basis for the mobile robot to walk accurately from the initial position to the working position and perform various tasks independently.

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Section: Introductionmentioning

confidence: 99%

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

Zhu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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“…shows an example of agent action space based on grid representation. Since space has been quantified as tiny grid units, the action of the agent only needs to decide which direction of the adjacent grid to move in the next moment [14]. At this time, the action of the agent at time t can be represented by a discrete value, for example, α t = 1 represents up, α t = 2 represents right, α t = 3 represents down, and α t = 4 represents left.…”

mentioning

confidence: 99%

Application Method of Environmental Protection Building Elements Based on Artificial Intelligence Technology in the Field of Urban Planning and Design

Yan¹

2022

Advances in Multimedia

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In order to improve the efficiency of urban planning and design and reduce the construction waste caused by inefficient design schemes, this paper proposes an application method of environmental protection building elements based on artificial intelligence technology in the field of urban planning and design. This model enables agents to learn how to interact with the environment with appropriate strategies, so as to generate the road network design scheme required by users. In order to better control the generation of the result scheme, this paper designs the rules and feedback of the agent. Among them, rules restrict the behavior of agents to avoid design schemes that do not meet the road design specifications. Feedback determines the values followed by agents, that is, the direction of agent strategy optimization. The experimental results show that after 4×105 iterations of the training process, the model is derived and used to generate 10000 plot road network schemes, the mean and variance of key indicators are calculated, and the scores of 153 plots with similar area and shape and the same land function in the same indicators from the real case base are calculated. From the performance of these indicators, it can be seen that the performance of the road network generated by the model is similar to that of the real road network in terms of traffic performance. Conclusion. the model completely retains the structural geographic information inside the plot, and the generated results can be directly applied to mainstream urban design software through data format conversion.

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“…Control feedback based on multiple tracking error inputs has been investigated in refs. [15][16][17][18], whereas alternative methods such as direct utilization of camera feeds or LiDAR measurements as inputs have also been explored in refs. [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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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A Hybrid Tracking Control Strategy for Nonholonomic Wheeled Mobile Robot Incorporating Deep Reinforcement Learning Approach

Cited by 19 publications

References 31 publications

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

Application Method of Environmental Protection Building Elements Based on Artificial Intelligence Technology in the Field of Urban Planning and Design

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

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