Lawn Mower Robot Route Planning With Grid Stay Costs
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Solution Overview
Problem
Existing lawn mower robots face inefficiencies due to repeated visits to the same areas, which can lead to lawn damage and reduced operational efficiency, especially when lacking wire-based boundary systems and being limited to specific trajectories.
Innovation Solution
A lawn mower robot equipped with a controller that uses gradient method-based route planning, assigning costs to grid maps based on environmental information and stay costs to minimize revisits, allowing efficient navigation within a defined area without the need for wire boundaries.
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 is deteriorated by repeatedly visiting the same place
Solution Approach 1:
The robot performs preliminary actions by setting stay costs for each grid cell before actual navigation. These pre-calculated cost values represent the probability or frequency of visiting each cell, allowing the robot to plan its path in advance to minimize revisits and maximize mowing efficiency.
Solution Approach 2:
The system dynamically changes the stay cost parameters for different grid cells based on environmental information and visitation history. By adjusting these cost parameters, the robot optimizes its navigation path to avoid frequently revisiting the same areas while ensuring complete coverage of the travel area.
2Reliability
If wire boundaries are installed to define the travel area, then the robot can navigate within defined limits, but the system complexity and installation requirements increase
Solution Approach 1:
The patent replaces the mechanical wire boundary system with a virtual boundary system implemented through software algorithms. The controller uses cost-based navigation to define and enforce travel area boundaries without requiring physical wires, thereby reducing system complexity and installation requirements while maintaining reliable boundary definition.
Solution Approach 2:
Instead of using physical wire boundaries, the system creates a virtual copy or representation of the boundary through digital grid maps and cost assignments. This virtual boundary system replicates the functionality of wire boundaries while eliminating the need for physical installation and reducing overall system complexity.
3Ease of operation
If the robot is limited to specific trajectories, then navigation is simplified, but adaptability to different environments and work patterns is reduced
Solution Approach 1:
The navigation system transitions from static predetermined trajectories to dynamic adaptive paths. The robot continuously calculates optimal routes based on real-time environmental information and accumulated stay costs, allowing it to adapt to different environments and work patterns while maintaining simplified operation through automated decision-making.
Solution Approach 2:
The travel area is segmented into discrete grid cells, each with associated cost values. This segmentation allows the robot to navigate flexibly by selecting sequences of cells based on cost minimization, providing both the simplicity of structured navigation and the adaptability to vary paths according to environmental conditions and work requirements.
Data Source
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.