Edgewise path selection method for robot obstacle crossing, chip, and robot
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Solution Overview
Problem
Conventional visual sweeping robots face challenges in navigating accurately due to low map precision and inaccurate obstacle marking, leading to frequent collisions during obstacle crossing.
Innovation Solution
An edgewise path selection method that plans an edgewise prediction path by selecting inflection points on a navigation path, calculating the minimum deviation path to guide the robot after collision, using left and right edgewise prediction paths to facilitate obstacle crossing with reduced deviation from the original path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional visual navigation is used, then the robot can navigate autonomously, but the navigation accuracy deteriorates due to low map precision and inaccurate obstacle marking
Solution Approach 1:
The patent introduces an edgewise prediction path as an intermediary between the navigation path and the robot's actual movement. This prediction path, generated by selecting inflection points and calculating minimum deviation paths, serves as a mediator that compensates for the imprecision in the original navigation map, allowing the robot to navigate accurately despite low map precision
Solution Approach 2:
The patent performs preliminary path planning by pre-selecting inflection points and pre-calculating edgewise prediction paths before the robot encounters obstacles. This preliminary action allows the robot to have ready-made alternative paths, improving navigation reliability when obstacles are detected without requiring real-time map updates
2Reliability
If the robot follows the navigation path strictly, then the path planning is simple, but the robot frequently collides with obstacles due to navigation inaccuracies
Solution Approach 1:
The patent segments the navigation path into multiple inflection points and creates multiple edgewise prediction paths (left and right paths) from each inflection point. This segmentation allows the robot to divide the complex obstacle avoidance problem into smaller, manageable path segments, improving collision avoidance while maintaining manageable complexity through systematic path generation
Solution Approach 2:
The patent changes the path selection parameter from a single fixed navigation path to multiple variable edgewise prediction paths with different deviation degrees. By calculating and comparing deviation degrees of different paths, the robot can dynamically select the optimal path, improving obstacle avoidance reliability without excessive complexity
3Reliability
If edgewise prediction paths are planned for obstacle crossing, then the robot can avoid obstacles more effectively, but the path planning complexity increases
Solution Approach 1:
The patent applies local quality by generating edgewise prediction paths only at specific inflection points where obstacles are detected, rather than planning complex paths throughout the entire navigation route. This localized approach improves obstacle crossing accuracy at critical points while keeping overall path planning complexity manageable by avoiding unnecessary computations in obstacle-free areas
Data Source
AI summary
An edgewise path selection method for robot obstacle crossing, a chip, and a robot. The method includes: first, planning an edgewise prediction paths for the robot obstacle crossing, and selecting, on a navigation path which is preset, preset inflection points satisfying a guide condition, and the navigation path formed by connecting inflection points is preset for the robot; the inflection points are used for guiding the robot to move to a final navigation target point; then according to information of distances between all the edgewise behavior points on each of the edgewise prediction path, and the preset inflection points satisfying the guide condition on one same navigation path, selecting one edgewise prediction path having a minimum deviation degree relative to the navigation path, so that the robot walks in an edgewise direction of the edgewise prediction path which is selected after colliding with an obstacle.