Robotic Lawn Mower Route Planning for Poor Terrain Avoidance
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Solution Overview
Problem
Conventional robotic lawn mowers lack efficient path planning and obstacle avoidance mechanisms, particularly in dynamic environments, leading to reduced efficiency and increased power consumption.
Innovation Solution
The robotic lawn mower employs a sensor assembly to collect environmental information, a processor to identify dynamic and static obstacles, and switches between a first and second map for navigation, where the second map has higher resolution and is updated in real-time, enabling adaptive path planning and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operation is used for lawn mowing, then the device complexity is low, but the working efficiency is low and power consumption is high
Solution Approach 1:
The robotic lawn mower performs self-navigation, self-mowing, and self-charging operations autonomously. The processor controls the traveling assembly to move the mower across the lawn, controls the drive assembly to perform mowing, and automatically returns to the charging dock when battery level is low, eliminating the need for manual operation and significantly improving working efficiency
Solution Approach 2:
The patent replaces manual mechanical operation with an automated control system. The processor substitutes human decision-making by receiving environmental information from sensors, identifying obstacles, planning paths, and controlling the traveling and drive assemblies automatically. This substitution of mechanical control with intelligent control systems resolves the contradiction between simplicity and efficiency
2Use of energy by moving object
If conventional path planning is used, then the device complexity is low, but power consumption is high
Solution Approach 1:
The robotic lawn mower employs sensor assemblies that continuously collect environmental information including obstacle detection, position monitoring, and battery level detection. The processor receives this feedback information in real-time and adjusts the traveling path and mowing strategy accordingly, optimizing energy consumption by avoiding obstacles and returning to charge when needed, thus resolving the contradiction between simple control and energy efficiency
Solution Approach 2:
The path planning system dynamically adjusts the mower's trajectory based on real-time environmental conditions. Rather than following a fixed predetermined path, the processor modifies the traveling route dynamically according to detected obstacles and battery status, enabling adaptive energy optimization that balances device complexity with power consumption reduction
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances working efficiency and safety by allowing the robotic lawn mower to navigate dynamically and statically changing environments effectively, reducing power consumption and improving user experience.
Implementation Method 1
a sensor assembly configured to collect environmental information around the robotic lawn mower
Implementation Method 2
The processor is further configured to identify an obstacle and determine whether the obstacle is static or dynamic based on the environmental information collected by the sensor assembly
Data Source
AI summary
A robotic lawn mower includes a drive assembly including a blade and a driving electric motor for driving the blade; a traveling assembly including traveling wheels and a traveling electric motor for driving the traveling wheels; a camera assembly configured to collect a two-dimensional image around the robotic lawn mower; and a processor communicatively or electrically connected to the camera assembly and configured to control an action of the robotic lawn mower according to at least the two-dimensional image; where the processor is further configured to identify a poor terrain condition in the two-dimensional image through semantic segmentation, where the poor terrain condition is one of multiple preset types of poor terrain conditions; verify the poor terrain condition according to a three-dimensional point cloud to obtain a verification result; and plan a traveling route of the robotic lawn mower according to the verification result.