Autonomous Mower Camera Adaptation for Grass Detection
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Solution Overview
Problem
Commercially available autonomous lawn mowers struggle with navigating in varying light conditions and avoiding obstacles, particularly under low light or high contrast scenarios, leading to potential damage to the mower and obstacles, and inefficient grass detection due to fixed camera parameters and inadequate sensor integration.
Innovation Solution
A control system for autonomous mowers that combines camera sensing with adaptive sensor and motor control, using a grass detection module to classify pixels, a sensor control module to adjust camera parameters and lighting, and a motor control module to optimize movement speed, enabling robust grass detection and obstacle avoidance across a range of lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed camera parameters are used for grass detection, then the device complexity is reduced, but the measurement precision of grass detection deteriorates under varying light conditions
Solution Approach 1:
The camera parameters (exposure time, gain, aperture) are made dynamic and adaptive rather than fixed. The control system continuously adjusts these parameters based on real-time lighting conditions detected by the camera, allowing the mower to maintain accurate grass detection across varying light environments from bright sunlight to low-light conditions.
Solution Approach 2:
The system changes camera parameters (exposure time, gain, aperture) according to lighting conditions. The control unit modifies these parameters dynamically to optimize the camera's performance in different illumination scenarios, thereby maintaining measurement precision without requiring overly complex hardware.
2Productivity
If the mower moves quickly to improve productivity, then the productivity increases, but motion blur in camera images increases reducing measurement precision
Solution Approach 1:
The system dynamically adjusts camera exposure time based on the mower's current speed. When the mower moves faster, the exposure time is increased to compensate for motion blur, allowing the camera to capture sufficient light and maintain image quality for accurate grass detection even at higher productivity speeds.
Solution Approach 2:
The control system uses feedback from the camera image quality and mower speed sensors to continuously optimize the exposure time. This closed-loop control ensures that the camera captures clear images of grass pixels regardless of the mower's speed, maintaining measurement precision while allowing high productivity operation.
3Measurement precision
If active lighting is added to improve grass detection in low light, then the measurement precision improves, but the use of energy increases
Solution Approach 1:
The active lighting system is controlled dynamically based on ambient light conditions. The control unit activates the lighting only when the camera detects that ambient light is insufficient for accurate grass detection, and adjusts the lighting intensity accordingly. This ensures improved measurement precision in low-light conditions while minimizing energy consumption by avoiding unnecessary lighting activation.
Solution Approach 2:
The system changes the lighting state (on/off) and intensity parameters based on detected ambient light levels and camera performance requirements. This adaptive control allows the mower to maintain accurate grass detection in varying light conditions while optimizing energy consumption by activating lighting only when truly necessary.
4Measurement precision
If exposure time is increased to reduce camera noise in low light, then the measurement precision improves, but the productivity decreases due to slower scanning
Solution Approach 1:
The exposure time is dynamically adjusted based on the mower's speed and ambient lighting conditions. When the mower moves slower, the exposure time can be increased to capture more light and reduce noise. When the mower moves faster, the exposure time is reduced accordingly. This dynamic coordination allows the system to maintain acceptable signal quality while preserving productivity.
Solution Approach 2:
The system changes the exposure time parameter in coordination with mower speed and lighting conditions. By adaptively adjusting this parameter, the system optimizes the balance between signal quality (measurement precision) and scanning speed (productivity), avoiding the need to always use long exposure times that would reduce productivity.
Data Source
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AI summary
The present invention presents a method and system for an autonomous mower attached with a camera, wherein the control of the parameters of the camera and the control of the mower movement and grass detection are optimized holistically during operation. The present invention mitigates the camera sensing limits by adapting the movement speed of the mower. Furthermore, the camera control optimizes the visibility of grass by using the grass mask of a grass segmentation to calculate updated exposure, gain and aperture values only from grass pixels. The grass segmentation tracks changes in the grass color that are caused by illumination differences. Optionally, the system is equipped with a head light used to further improve the camera signal quality in conjunction with the control of the camera parameters and the movement speed of the mower.