Local obstacle avoidance walking method of self-moving robot
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Solution Overview
Problem
Existing self-moving robots using random walking methods for obstacle avoidance result in inefficient paths, excessive energy consumption, and poor cleaning effectiveness due to missed regions around obstacles.
Innovation Solution
A local obstacle avoidance walking method for self-moving robots that involves translating perpendicular to the initial direction when an obstacle is detected, and determining whether to continue in the original direction based on sensor feedback, thereby optimizing path efficiency and reducing missed regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot uses random walking method to avoid obstacles, then the robot can eventually avoid obstacles, but the walking path becomes random and complex, wasting time and power
Solution Approach 1:
The patent segments the obstacle avoidance process into distinct phases: detection phase (sensing obstacle), translation phase (moving perpendicular to original direction), and continuation phase (resuming original direction if path is clear). This segmentation transforms the random walking method into a structured approach, reducing unnecessary movements and time loss while maintaining reliable obstacle avoidance.
2Reliability
If the robot uses random walking method to avoid obstacles, then the robot can eventually avoid obstacles, but the walking path becomes complex, consuming excessive power
Solution Approach 1:
The patent segments the obstacle avoidance process into distinct phases: detection phase (sensing obstacle), translation phase (moving perpendicular to original direction), and continuation phase (resuming original direction if path is clear). This segmentation transforms the random walking method into a structured approach, reducing unnecessary movements and power consumption while maintaining reliable obstacle avoidance.
3Reliability
If the robot moves back and turns repeatedly to avoid obstacles, then the robot can avoid obstacles, but the walking tracks become random and complex
Solution Approach 1:
The patent segments the obstacle avoidance process into distinct phases: detection phase (sensing obstacle), translation phase (moving perpendicular to original direction), and continuation phase (resuming original direction if path is clear). This segmentation transforms the random walking method into a structured approach, simplifying the walking track pattern while maintaining reliable obstacle avoidance.
4Productivity
If the robot continues to walk after avoiding local obstacle, then the robot resumes operation, but missing regions are left behind around the obstacle
Solution Approach 1:
The patent applies dimensionality change by translating the robot in a second direction (perpendicular to the first walking direction) when an obstacle is detected. This perpendicular translation allows the robot to bypass obstacles while maintaining coverage of the working region, preventing missing regions from forming. After translation, the robot can continue in the original first direction, ensuring both operational efficiency and complete cleaning coverage.
Data Source
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AI summary
A local obstacle avoidance walking method of a self-moving robot, comprising: step 100: the self-moving robot walks in a first direction, and when an obstacle is detected, the self-moving robot translates for a displacement M1 in a second direction perpendicular to the first direction; and step 200: determining whether the self-moving robot is able to continue to walk in the first direction after the translation, if a result of the determination is positive, the self-moving robot continues to walk in the first direction, and if the result of the determination is negative, the self-moving robot acts according to a preset instruction. The method enables the robot to accurately avoid a local obstacle, provides a concise walking route, shortens the determination time, and improves the working efficiency of the self-moving robot.