Mobile Robot Navigation Using Fuzzy Logic for Obstacle Avoidance
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Solution Overview
Problem
Current robot navigation systems face challenges in ensuring stable and autonomous navigation within complex home environments, particularly in avoiding obstacles, due to limitations in spatial information processing and obstacle detection.
Innovation Solution
A navigation system comprising a processor and multiple distance measuring modules that set waypoints, calculate distances, and adjust movement speeds and directions using fuzzy theory algorithms to prevent collisions with obstacles, allowing the robot to navigate safely through defined paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot uses traditional navigation methods without fuzzy theory algorithms, then the navigation system is simpler, but the robot cannot accurately determine optimal traveling speeds and turning coefficients, leading to collision risks in complex home environments
Solution Approach 1:
The patent applies fuzzy theory algorithms to dynamically adjust navigation parameters (traveling speed and turning coefficient) based on real-time spatial information and obstacle distances. This transforms the navigation system from using fixed parameters to adaptive parameters, enabling reliable collision prevention in complex home environments while managing system complexity through mathematical modeling.
2Measurement precision
If the robot uses multiple distance measuring modules and fuzzy theory algorithms to accurately detect obstacles, then the collision prevention capability is improved, but the spatial information processing complexity increases
Solution Approach 1:
The patent divides the navigation system into multiple independent distance measuring modules positioned at different locations on the robot. Each module independently measures distance to obstacles in its specific direction, and the processor integrates these segmented measurements into comprehensive spatial information. This segmentation enables accurate 360-degree obstacle detection while managing processing complexity through modular architecture.
Solution Approach 2:
The system implements continuous feedback loops where distance measuring modules constantly monitor obstacle positions, the processor analyzes spatial information using fuzzy theory algorithms, and the navigation parameters are dynamically adjusted based on this feedback. This closed-loop feedback mechanism enables precise obstacle detection and adaptive navigation, resolving the contradiction between measurement precision and processing complexity.
3Ease of operation
If the robot navigates autonomously without adaptive speed and turning adjustments, then the navigation system is easier to operate, but the autonomous navigation stability decreases in complex home environments
Solution Approach 1:
The navigation system implements self-service through autonomous operation combined with adaptive control. The robot independently navigates home environments using onboard sensors and processors, and the fuzzy theory algorithms enable self-adjustment of traveling speed and turning coefficients based on real-time conditions. This self-service capability maintains ease of operation (no manual control needed) while achieving navigation stability through adaptive parameter adjustment.
Data Source
AI summary
A navigation system adapted to an electronic device is provided. The navigation system comprises: a processor configured to control a movement direction of the electronic device according to a navigation path, obtain spatial information, and set a waypoint according to the spatial information; and a plurality of distance measuring modules for measuring a waypoint distance between the electronic device and the waypoint. When the processor determines the waypoint distance is less than a waypoint threshold value, the processor is configured to calculate a first distance and a second distance according to the obstacle distances measured by the distance measuring modules. The processor is configured to control the electronic device to have a first movement, a second movement and a third movement according to the first distance and the second distance. A navigation method is further provided.