Experimental evaluation of new navigator of mobile robot using fuzzy Q-learning

Lachekhab, Fadhila; Tadjine, Mohamed; Kesraoui, Mohamed

doi:10.1504/ijesms.2019.10023261

Cited by 2 publications

(3 citation statements)

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“…So far, the existing algorithms include the TD(λ) algorithm [21], the Q-Learning, the Sarsa algorithm [22], and the Actor-Chile algorithm [23], among others. Reinforcement learning has been generalized and applied to the field of robotics, such as navigation [24], trajectory tracking [25,26], path planning [27], balance control [28,29], decision-making [30].…”

Section: Related Workmentioning

confidence: 99%

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Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

2021

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Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

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Section: Related Workmentioning

confidence: 99%

“…to the field of robotics, such as navigation [24], trajectory tracking [25,26], path planning [27], balance control [28,29], decision-making [30].…”

Section: Multi-robot Motion Planning System Structurementioning

confidence: 99%

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

2021

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“…Alternatively, this method’s implementation combined with fuzzy logic ( Tuazon et al, 2016 ) and reinforcement learning (RL) ( Liu, Qi & Lu, 2017 ) is another approach employed for robot navigation. Other alternatives to solve the path planning are addressed using NN ( Wei, Tsai & Tai, 2019 ) or FQL ( Lachekhab, Tadjine & Kesraoui, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots

López-Lozada

Rubio-Espino

Azuela

et al. 2021

PeerJ Computer Science

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Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot’s prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.

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Experimental evaluation of new navigator of mobile robot using fuzzy Q-learning

Cited by 2 publications

References 0 publications

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots

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