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

Lachekhab, Fadhila; Tadjine, Mohamed; Kesraoui, Mohamed

doi:10.1504/ijesms.2019.101670

Cited by 1 publication

(1 citation statement)

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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: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%