Abstract
Mobile robots are endeavoring toward full autonomy. To that end, wheeled mobile robots have to function under non-holonomic constraints and uncertainty derived by feedback sensors and/or internal dynamics. Speed control is one of the main and challenging objectives in the endeavor for efficient autonomous collision-free navigation. This paper proposes an intelligent technique for speed control of a wheeled mobile robot using a combination of fuzzy logic and supervised machine learning (SML). The technique is appropriate for flexible leader-follower formation control on straight paths where a follower robot maintains a safely varying distance from a leader robot. A fuzzy controller specifies the ultimate distance of the follower to the leader using the measurements obtained from two ultrasonic sensors. An SML algorithm estimates a proper speed for the follower based on the ultimate distance. Simulations demonstrated that the proposed technique appropriately adjusts the follower robot’s speed to maintain a flexible formation with the leader robot.
Highlights
With increasing computing power, fast progress in sensor and actuator design, and low-cost production, robotic systems have been in ever-increasing demand and application
The technique consists of a fuzzy controller and an supervised machine learning (SML) algorithm to specify the ultimate distance to the leader and the speed of the follower, respectively, in order to maintain the formation with the leader robot
The fuzzy controller determines the ultimate distance between the follower robot and the leader vehicle based on data readings of the ultrasonic sensors
Summary
Fast progress in sensor and actuator design, and low-cost production, robotic systems have been in ever-increasing demand and application. Among intelligent and knowledge-based techniques, fuzzy decision-making plays a vital role in robot navigation with imprecise, incomplete, and vague sensor measurements [14,15]. This paper presents an intelligent technique for the speed control of a wheeled mobile robot using a combination of fuzzy decision-making and SML. The follower’s speed is adjusted via the fuzzy controller and the SML algorithm employed in the proposed technique. The fuzzy controller uses both ultrasonic sensory measurements to determine the ultimate distance of the follower to the leader. Applying two ultrasonic sensors to enhance the accuracy of the distance measurements Determination of the ultimate distance with the fuzzy controller based on the measurements from both ultrasonic sensors Specifying the speed of the (follower) robot with the SML algorithm based on the ultimate distance.
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