A Fuzzy Logic Method for Autonomous Robot Navigation in Dynamic and Uncertain Environment Composed with Complex Traps

Abstract

Nowadays, mobile robotics is becoming one of the most open research areas due to the fact that it includes many quite complex advanced mechanisms that are difficult to get a thorough mastery of. In fact, the control task becomes much more complex. The problem is how to guarantee safety for the robot while moving toward a known goal. For this reason, simulation plays a very important role in robotics. It is believed to be the most suitable procedure that may offer a fast prototyping of mobile robots, by considering all options even before fixing the technological devices. Multi-agent simulation models are widely used to solve problems in mobile robotic. In fact, there are similarities between the agent and the robot. However, the most popular methods are based on reactive local navigation schemes that tightly couple the robot actions to the sensor information. But the robot needs not only to avoid collision at real time, but also to anticipate environment evolution in order to take suitable decisions. In this paper, we present a multi-agent simulation model of an autonomous robot that takes into account the reactivity and the anticipation of blocking situations in dynamic and uncertain environments by using only ultrasonic sensors.

Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies

“…A real-time fuzzy monitoring controller for robot navigation targets using infrared sensors [14]. Introduced a smart agent-based fuzzy logic structure that lets the robots travel on its self in an unpredictable domain without human interaction [15]. The path modeling technique using a fuzzy controller is created [16].…”

“…14 Introduced a smart agent-based fuzzy logic structure that lets the robots travel on its self in an unpredictable domain without human interaction. 15 The path modeling technique using a fuzzy controller is created. 16 Zhu and Yang 17 suggested the reactive-based neuro-fuzzy navigational strategy for portable robots.…”

Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

“…In the local path planning, the robot decides its motion and orientation autonomously using different equipped sensors. Fuzzy logic (Ayari et al , 2010; Yousfi et al , 2010), neural network (NN) (Algabri et al , 2015), neuro-fuzzy (Zhu and Yang, 2007; Algabri et al , 2015), genetic algorithm (GA) (Liu et al , 2006; Algabri et al , 2015), bacterial foraging optimization (Hossain and Ferdousand, 2015; Montiel et al , 2015) and fuzzy-wind driven optimization algorithm (Pandey and Parhi, 2017), etc., are successfully used by various authors to solve the local path planning problems for mobile robot navigation. The authors (Zhu and Yang, 2007) have proposed a novel neuro-fuzzy sensor-based reactive navigation of mobile robots in unknown environments.…”

“…In that paper, the authors have used the NN technique to tune the membership function parameters of fuzzy logic. The authors (Ayari et al , 2010) have developed a multi-agent fuzzy logic intelligent control system, which trained the robot to navigate autonomously in dynamic and uncertain environments. In Yousfi et al (2010), the authors have presented the gradient method-based optimal Takagi–Sugeno fuzzy controller to tune the membership function parameters and implemented this for mobile robot navigation and obstacle avoidance.…”

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

“…The designed fuzzy rules are able to emulate the human driving behavior. Ayari et al [51] have developed a multi-agent fuzzy logic intelligent control system, which trains the robot to navigate autonomously in dynamic and uncertain environments.…”