Mobile Robot Route Planning With Grid Stay Costs
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Solution Overview
Problem
Existing route planning and traveling methods for lawn mower robots are inefficient and prone to lawn damage due to repeated visits to the same points.
Innovation Solution
A moving robot equipped with a grid map and a controller that assigns cost information based on environmental factors and travel history, allowing the robot to generate optimal routes by minimizing revisits to the same points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the lawn mower robot travels randomly in the travel area, then the robot can cover the entire area, but efficiency deteriorates due to repeatedly visiting the same place
Solution Approach 1:
The robot performs preliminary actions by setting cost values for each grid cell before traveling. The cost values represent the expense of visiting each cell, and the robot uses these pre-established cost values to plan its travel route, avoiding random movement and ensuring efficient coverage of the entire travel area without repeated visits to the same places
Solution Approach 2:
The robot implements feedback by updating the cost values of visited grid cells during travel. After visiting a cell, the robot increases its cost value, creating a feedback mechanism that guides future route planning. This feedback system ensures the robot naturally avoids previously visited areas and efficiently covers new regions, resolving the contradiction between area coverage and efficiency
2Reliability
If the robot uses wire-based magnetic field sensing for area limitation, then movement can be restricted to the travel area, but the system cannot be applied to wireless methods
Solution Approach 1:
The patent replaces the mechanical/wire-based magnetic field sensing system with a grid map-based virtual boundary system. Instead of using physical wires that generate magnetic fields, the robot uses a digital grid map representation of the travel area and cost-based route planning to achieve area limitation. This substitution enables wireless operation while maintaining reliable area boundary control through software-based navigation constraints
Solution Approach 2:
The system changes the fundamental parameter from magnetic field strength detection to grid cell cost value manipulation. By transforming the boundary control mechanism from physical magnetic field sensing to digital cost parameter management, the system achieves wireless adaptability while maintaining reliable area restriction through the grid map and cost function approach
3Ease of operation
If the robot follows preset trajectories for returning to charger, then return path is determined, but it cannot be used for other travels such as lawn mowing work
Solution Approach 1:
The patent implements a universal grid map-based cost function system that serves multiple functions. The same grid map representation and cost value manipulation mechanism used for return-to-charger navigation is also applied to lawn mowing work and other travel tasks. This universal approach allows the robot to flexibly determine optimal paths for different tasks by adjusting cost values according to task-specific requirements, eliminating the need for separate preset trajectories for different operations
Data Source
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AI summary
A moving robot according to an aspect of the present invention includes a body configured to define an exterior, a travelling unit configured to move the body against a travelling surface of a travelling area, a storage configured to store a grid map corresponding to a travelling area and cost information of grids included in the grid map, and a controller configured to generate a movement route based on the cost information, control the travelling unit to travel according to the generated movement route, and increase a stay cost of a grid corresponding to a route that has passed during the travelling and control the storage to store the increased stay cost.