Robot Lawnmower Perimeter Teaching for Reliable Boundary Confinement
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Solution Overview
Problem
Existing autonomous lawn mowers rely on complex systems of sensors and barriers for navigation and confinement, which can be inefficient and prone to errors, especially when dealing with irregular lawn shapes and obstacles.
Innovation Solution
An autonomous lawn mower equipped with a sensor system including edge following sensors, grass sensors, and a navigation system that uses boundary markers and an operator feedback unit to learn and map the lawn perimeter, allowing for efficient and accurate navigation and confinement within defined areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex systems of sensors and barriers are used for navigation and confinement, then navigation accuracy and confinement reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces boundary markers as intermediary objects placed in the environment to facilitate robot navigation and confinement. These markers serve as mediators between the robot's sensors and the physical boundaries, enabling reliable confinement through simple optical detection rather than complex sensor systems or physical barriers
Solution Approach 2:
The patent extracts the confinement function from complex sensor systems and physical barriers, isolating it into simple boundary markers that define the operational area. This separation allows the robot to use basic sensors to detect markers rather than requiring complex integrated systems
2Measurement precision
If complex systems of sensors and barriers are used for navigation and confinement, then navigation accuracy is improved, but device complexity increases
Solution Approach 1:
Boundary markers act as intermediaries that enhance navigation accuracy by providing distinct, easily detectable reference points in the environment. The markers enable precise position detection using simple optical sensors rather than requiring complex sensor arrays or active beacon systems
Solution Approach 2:
The patent uses passive optical markers that can be detected by the robot's vision system, creating a simplified representation of boundary information. This approach replaces complex active beacons or multiple sensor types with simple visual cues that provide sufficient navigation precision
3Ease of operation
If random motion confinement methods are used, then ease of operation is improved, but productivity and mowing efficiency deteriorate
Solution Approach 1:
The patent implements preliminary localization and mapping actions before the mowing operation begins. The robot first localizes itself relative to boundary markers and creates a map of the mowing area, enabling subsequent efficient path planning rather than relying on random motion throughout the entire operation
Solution Approach 2:
The patent transitions the robot from static random motion patterns to dynamic, adaptive path planning based on localized environmental information. The robot dynamically adjusts its trajectory using detected boundary markers and map data, optimizing mowing efficiency while maintaining ease of operation through autonomous adaptation
Data Source
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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.