Robot Lawnmower Teach Routine for Wire-Free Perimeter Mapping
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Solution Overview
Problem
Existing autonomous lawn mowers lack an efficient method to accurately define and maintain boundaries within a lawn area, often relying on complex systems of sensors and beacons, and may not effectively handle changes in terrain or obstacles.
Innovation Solution
A robot lawnmower system that includes a teach routine allowing an operator to manually define the perimeter of the mowing area, using a localization system and controller to store perimeter positions and detect unteachable states, with sensors and feedback mechanisms to ensure accurate boundary tracing and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex systems of sensors and beacons are used to define boundaries, then boundary detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts the boundary definition function from complex sensor systems and concentrates it in a simple teachable perimeter system. The operator manually guides the robot to trace the perimeter, and the controller stores these boundary points, separating the complex detection task from the robot's onboard systems.
Solution Approach 2:
The perimeter is taught to the robot in advance through manual guidance before autonomous operation begins. The controller stores the perimeter positions during a teaching phase, allowing the robot to later autonomously navigate within the pre-defined boundaries without real-time complex detection.
2Ease of operation
If random motion is used within a confined area, then ease of operation is improved, but productivity decreases
Solution Approach 1:
The robot transitions from static random motion to dynamic purposeful navigation. The controller dynamically adjusts the robot's path based on the taught perimeter and internal localization, enabling the robot to systematically cover the mowing area while maintaining operational simplicity through autonomous decision-making.
Solution Approach 2:
The robot uses its localization system to continuously monitor its position relative to the taught perimeter and adjusts its motion accordingly. This feedback mechanism enables productive systematic mowing patterns while maintaining ease of operation through autonomous control.
3Measurement precision
If continuous guide conductor boundary is used, then boundary definition is improved, but ease of manufacture worsens
Solution Approach 1:
The patent replaces the mechanical guide conductor system with an electronic/perceptual boundary definition system. Instead of physical conductors embedded in the ground, the robot uses its localization system to detect and store perimeter positions, eliminating the need for complex physical installation while maintaining accurate boundary definition.
Solution Approach 2:
The perimeter is defined through a teaching process that occurs before autonomous operation. The operator guides the robot along the desired perimeter, and the controller stores these positions, creating a virtual boundary that requires no physical installation infrastructure.
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A robot lawnmower (10) includes a robot body (100), a drive system (400), a localizing system (550), a teach monitor (600), and a controller (150) in communication with one another. The drive system is configured to maneuver the robot lawnmower over a lawn. The teach monitor determines whether the robot lawnmower is in a teachable state. The controller includes a data processing device (152a) and non-transitory memory (152b) in communication with the data processing device. The data processing device executes a teach routine (155) when the controller is in a teach mode for tracing a confinement perimeter (21) around the lawn (20) as a human operator pilots the robot lawnmower, when the robot lawnmower is in the teachable state, the teach routine stores global positions determined by the localizing system in the non-transitory memory, and when the robot lawnmower is in the unteachable state, the teach routine issues an indication of the unteachable state.