Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors

Al-Mutib, Khalid; Abdessemed, Foudil

doi:10.25046/aj020342

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…For the purpose of comparison, the online navigation schemes for local path planning proposed in [10] and [18] were also applied to the same environment in Fig. 12 by using the same FCs.…”

Section: Simulation Results Of the Fast Navigation Schemementioning

confidence: 99%

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Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning

Juang

Chou

Lin

2022

IEEE Trans. Cybern.

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This paper proposes a fast navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This paper formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportionalderivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this paper presents simulations and experiments to verify the feasibility and advantages of the fast navigation scheme.

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“…For the purpose of comparison, the online navigation schemes for local path planning proposed in [10] and [18] were also applied to the same environment in Fig. 12 by using the same FCs.…”

Section: Simulation Results Of the Fast Navigation Schemementioning

confidence: 99%

“…12 shows the trajectories of the two navigation schemes used for comparison. The navigation scheme in [10] faced the dead-cycle problem around U-shaped obstacles, as shown in Fig. 12.…”

Section: Simulation Results Of the Fast Navigation Schemementioning

confidence: 99%

Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning

Juang

Chou

Lin

2022

IEEE Trans. Cybern.

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“…The motor speed is controlled using a proportional-integral-derivative (PID) method to follow the wall. The wall following method has recently become an interesting topic which can help navigate robots in a messy or disorderly environment [29,30]. The robot travels along contour of the object with a certain distance.…”

Section: Methodsmentioning

confidence: 99%

2D mapping using omni-directional mobile robot equipped with LiDAR

2020

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A room map in a robot environment is needed because it can facilitate localization, automatic navigation, and also object searching. In addition, when a room is difficult to reach, maps can provide information that is helpful to humans. In this study, an omni-directional mobile robot equipped with a LiDAR sensor has been developed for 2D mapping a room. The YDLiDAR X4 sensor is used as an indoor scanner. Raspberry Pi 3 B single board computer (SBC) is used to access LiDAR data and then send it to a computer wirelessly for processing into a map. This computer and SBC are integrated in robot operating system (ROS). The movement of the robot can use manual control or automatic navigation to explore the room. The Hector SLAM algorithm determines the position of the robot based on scan matching of the LiDAR data. The LiDAR data will be used to determine the obstacles encountered by the robot. These obstacles will be represented in occupancy grid mapping. The experimental results show that the robot is able to follow the wall using PID control. The robot can move automatically to construct maps of the actual room with an error rate of 4.59%.

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“…The angles of right and left wheels, as well as the distance to the goal point, are taken as input membership functions, and the turn angle is the output membership function with upper and lower limits. Khalid-Al-Mutib et al [94] also introduced a Type-2 fuzzy controller for mobile robot navigation. Santiago et al [95] investigated Type-2 Fuzzy control for mobile robot navigation.…”

Section: Control Strategies For Overcoming the Effects Of Terrain Variationsmentioning

confidence: 99%

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Nampoothiri

Sunny

et al. 2021

SN Appl. Sci.

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The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.

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Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors

Abstract: This paper presents the development and design of a methodology based on fuzzy logic to

Cited by 9 publications

References 17 publications

Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning

Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning

2D mapping using omni-directional mobile robot equipped with LiDAR

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

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