Application of fuzzy control to a sonar-based obstacle avoidance mobile robot

Thongchai, Siripun; Kawamura, K.

doi:10.1109/cca.2000.897461

Cited by 41 publications

(14 citation statements)

References 12 publications

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“…The sonars determine distances to objects by measuring the time-of-flight (TOF) between an emitted ultrasonic burst and its echo. However, sonars commit errors when the incidence angle is bigger than 45 or when the reflecting surface is too small to return enough signal. In order to overcome those problems, a combination of sonic and IR sensors was explored.…”

Section: Introductionmentioning

confidence: 99%

“…The FLC methodology for mobile robots is described by Omrane et al, 43 and there are several smart wheelchair prototypes that use an FLC as the main control technique and range sensors as inputs. 10,11,28,[44][45][46] The proposed FLC runs in the FPGA at a high-speed rate and guarantees the wheelchairs reactions on-time, as this system acquires data from sensors and makes decisions within 46.16 times per second.…”

Section: Introductionmentioning

confidence: 99%

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A fuzzy logic navigation controller implemented in hardware for an electric wheelchair

Rojas

Ponce

Molina

2018

International Journal of Advanced Robotic Systems

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In this article, we present an obstacle avoidance controller implemented in a field programmable gate array for an electric wheelchair. It is based on a traditional approach with ultrasonic sensors and fuzzy logic. Various tests were conducted to characterize the prototype and to evaluate the controller performance. The results showed that the system is able to acquire data from sensors and make decisions 46.16 times per second. The sensors' coverage extends 3 m to the front, rear, left, and right sides of the wheelchair; moreover, the sensors detect 0.95-cm diameter objects at 40 cm. The power consumption was evaluated, and it was found that the hardware architecture reduces the battery life by only 0.87%. Furthermore, the controller helped to navigate in confined areas, avoiding obstacles with cautious movements and decreasing the likelihood of collision. The proposed methodology uses data from eight sonars distributed around the wheelchair to make navigation decisions, besides the hardware-based architecture guarantees real-time control and on-time response.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fuzzy logic navigation controller implemented in hardware for an electric wheelchair

Rojas

Ponce

Molina

2018

International Journal of Advanced Robotic Systems

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“…Ultrasonic sensors can detect the distance between a robot and obstacles [14][15][16]. Global positioning systems can detect the robot's current position [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Controller Design for Intelligent Robots

Chen

Wang

et al. 2017

Mathematical Problems in Engineering

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This paper presents a fuzzy logic controller by which a robot can imitate biological behaviors such as avoiding obstacles or following walls. The proposed structure is implemented by integrating multiple ultrasonic sensors into a robot to collect data from a realworld environment. The decisions that govern the robot's behavior and autopilot navigation are driven by a field programmable gate array-(FPGA-) based fuzzy logic controller. The validity of the proposed controller was demonstrated by simulating three realworld scenarios to test the bionic behavior of a custom-built robot. The results revealed satisfactorily intelligent performance of the proposed fuzzy logic controller. The controller enabled the robot to demonstrate intelligent behaviors in complex environments. Furthermore, the robot's bionic functions satisfied its design objectives.

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“…We use the minoperation rule for fuzzy implication. Finally, the output for angular velocity is given by equation (3). Figure 3 shows an example of a fuzzy inference system for obstacle avoidance using sonar numbers 5 and 9.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The application of fuzzy control to a sonar-based obstacle avoidance for HelpMate has been implemented successfully [3]. All sonar sensors send data to the inputs of fuzzy controllers.…”

Section: Introductionmentioning

confidence: 99%

Sonar behavior-based fuzzy control for a mobile robot

Thongchai

Suksakulchai²,

Wilkes³

et al.

SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to S

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This paper describes how fuzzy control can be applied to a sonar-based mobile robot. Behavior-based fuzzy control for HelpMate behaviors was designed using sonar sensors. The fuzzy controller provides a mechanism for combining sensor data from all sonar sensors which present di®erent information.The behavior-based approach is implemented as an individual high priority behavior. The highest level behavior is called the task-oriented behavior, which consists of two subtasks, wall following and goal seeking. The middle level behavior is obstacle avoidance. The lowest level is an emergency behavior. Each behavior was built as an atomic agent based on the intelligent machine architecture (IMA). The results demonstrate that each behavior works correctly. The HelpMate robot can follow the wall, go to the goal, and avoid obstacles detected by the sonar sensors.

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Application of fuzzy control to a sonar-based obstacle avoidance mobile robot

Cited by 41 publications

References 12 publications

A fuzzy logic navigation controller implemented in hardware for an electric wheelchair

A fuzzy logic navigation controller implemented in hardware for an electric wheelchair

Fuzzy Logic Controller Design for Intelligent Robots

Sonar behavior-based fuzzy control for a mobile robot

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