Mobile Robot Path Planning Based on Optimized Fuzzy Logic Controllers

Cherroun, Lakhmissi; Boumehraz, Mohamed; Kouzou, Abdellah

doi:10.1007/978-981-13-2212-9_12

Cited by 15 publications

(9 citation statements)

References 24 publications

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“…Theoretically speaking, the idea of an interval type-2 fuzzy logic system is well-depicted in [33,34]. Thus, a short explanation of the interval type-2 FLS is mentioned in this section.…”

Section: Fundamentals Of Interval Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Ngo

Nguyеn

2022

Journal of Robotics and Control

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Robot manipulator has become an exciting topic for many researchers during several decades. They have investigated the advanced algorithms such as sliding mode control, neural network, or genetic scheme to implement these developments. However, they lacked the integration of these algorithms to explore many potential expansions. Simultaneously, the complicated system requires a lot of computational costs, which is not always supported. Therefore, this paper presents a novel design of switching mechanisms to control the robot manipulator. This investigation is expected to achieve superior performance by flexibly adjusting various strategies for better selection. The Proportional-Integral-Derivative (PID) scheme is well-known, easy to implement, and ensures rapid computation while it might not have much control effect. The advanced interval type-2 fuzzy sliding mode control properly deals with nonlinear factors and disturbances. Consequently, the PID scheme is switched when the tracking error is less than the threshold or is far from the target. Otherwise, the interval type-2 fuzzy sliding mode control scheme is activated to cope with unknown factors. The main contributions of this paper are (i) the recommendation of a suitable switching mechanism to drive the robot manipulator, (ii) the successful integration of the interval type-2 fuzzy sliding mode control to track the desired trajectory, and (iii) the launching of several tests to validate the proposed controller with robot model. From these achievements, it would be stated that the proposed approach is effective in tracking performance, robust in disturbance-rejection, and feasible in practical implementation.

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“…Theoretically speaking, the idea of an interval type-2 fuzzy logic system is well-depicted in [33,34]. Thus, a short explanation of the interval type-2 FLS is mentioned in this section.…”

Section: Fundamentals Of Interval Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Ngo

Nguyеn

2022

Journal of Robotics and Control

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“…Dijkstra algorithm [21] has the defect of high search time without considering the target information in the global space. In addition, many intelligent algorithms, such as fuzzy logic [22], genetic algorithm (GA) [23], and neural network [24], [25] have been used for path planning of mobile robots [26], [27]. Among the existing methods to solve the global path optimization problem, GA shows strong robust optimization performance by simulating the natural evolution of the population.…”

Section: Related Work a Intelligent Path Planningmentioning

confidence: 99%

An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

Chen

Jiang

et al. 2020

IEEE Access

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Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.

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“…Fuzzy logic control (FLC) presents an advantageous alternative in these circumstances because it limits the inputs within the range of the predefined membership functions as demonstrated in refs. [25][26][27][28][29][30][31][32][33][34][35]. The membership functions are typically designed to overlap with one another.…”

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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Mobile Robot Path Planning Based on Optimized Fuzzy Logic Controllers

Cited by 15 publications

References 24 publications

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

Enhancement of the Tracking Performance for Robot Manipulator by Using the Feed-forward Scheme and Reasonable Switching Mechanism

An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

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

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