Abstract
A Lookup-Table (LUT) based design enhances the processing speed of a fuzzy obstacle avoidance controller by reducing the operation time. Also, a LUT sharing method provides efficient ways of reducing the LUT memory size. In order to share the LUT which is used for a fuzzy obstacle avoidance controller, an idea of using a basis function is developed. As applications of the shared LUT-based fuzzy controller, a laser-sensor-based fuzzy controller and an ultrasonic-sensor-based fuzzy controller are introduced in this paper. This paper suggests a LUT sharing method that reduces the LUT buffer size without a significant degradation of the performance. The LUT sharing method makes the buffer size independent of the fuzzy system's complexity. A simulation using MSRDS (Microsoft Robotics Developer Studio) is used to evaluate the proposed method. To investigate the performance of the controller, experiments are carried out using a Pioneer P3-DX with LabVIEW as an integration tool. Although the simulation and experiments show little difference between the fully valued LUT-based method and the LUT sharing method in terms of the operation time, the LUT sharing method reduces almost 95% of the full-valued LUT-based buffer size.
Highlights
Autonomous navigation algorithms are applied to various applications for mobile robots to drive toward their target position without a remote control program
The output fuzzy sets Hard Left (HL), Left (L), Soft Left (SL), Straight (S), Soft Right (SR), Right (R), and Hard Right (HR) are for positive rules, while HL, L, SL, S, SR, R, and HR are for negative rules
It is necessary to reduce the operation clock frequency to lower the power consumption because a high clock frequency elevates the power consumption of the system. All of these factors make it clear that a reduction of the operational complexity is a better answer for both faster operation and lower power consumption
Summary
Autonomous navigation algorithms are applied to various applications for mobile robots to drive toward their target position without a remote control program. As a result of these papers, fuzzy controller methods have more advantages in real‐ time applications because they provide simpler and more intuitive methods of robot obstacle avoidance. Lilly’s work provided a more intuitive method for robot obstacle avoidance through its use of negative fuzzy rules to eliminate redundant fuzzy rules. The P/N rule fuzzy system can be applied to obstacle avoidance for the autonomous navigation of mobile robots. We adopted a P/N fuzzy controller method which is simpler and more intuitive for robot obstacle avoidance. If the fuzzy controller obtains each membership degree from LUT buffers, the robot can reduce the processing time and perform more frequent obstacle‐ avoidance operations because the operation delay in the multiplication of the floating point operands is eliminated
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