Adaptive Mowing Robot Vision Control for Obstacle Avoidance
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Solution Overview
Problem
Existing mowing robots have poor obstacle avoidance efficiency and trajectory control accuracy, leading to significant time wastage during large-scale mowing tasks, and are susceptible to interference from ambient lighting conditions, resulting in low obstacle recognition accuracy.
Innovation Solution
A method for adaptive mowing control using a control system that acquires real-time environment images and trajectory information, adjusts effective imaging distance based on lighting conditions, performs block segmentation, and employs a Support Vector Machine (SVM) model for obstacle identification and proactive avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional contact sensors and wire guidance technology are used for obstacle avoidance, then the mowing robot can perform basic navigation, but the obstacle avoidance efficiency is poor and trajectory control accuracy is low
Solution Approach 1:
The patent replaces traditional mechanical contact sensors and wire guidance systems with a deep learning-based visual recognition system. The mowing robot uses an imaging device to capture environmental images, and a deep learning model processes these images to identify obstacles and generate avoidance trajectories, substituting mechanical sensing with optical and computational methods.
Solution Approach 2:
The patent dynamically adjusts the effective imaging distance parameter based on ambient lighting conditions. A photosensitive sensor detects light intensity, and the control system modifies the effective imaging distance accordingly - extending it in bright conditions and shortening it in dim conditions - to optimize both obstacle detection accuracy and processing efficiency.
2Reliability
If sensors are used to perceive environmental information, then the mowing robot can detect obstacles, but the sensors are prone to false detections and missed detections due to insufficient environmental perception
Solution Approach 1:
The patent implements dynamic adjustment of the effective imaging distance based on real-time ambient lighting conditions detected by a photosensitive sensor. The control system continuously modifies the imaging parameters to adapt to changing environmental conditions, ensuring optimal obstacle detection accuracy across varying light levels while reducing false and missed detections.
3Productivity
If the mowing robot operates at high speed to improve productivity, then the mowing efficiency increases, but the trajectory control accuracy decreases and time is wasted on corrections
Solution Approach 1:
The patent performs preliminary obstacle identification and trajectory planning using deep learning image processing before the mowing robot reaches the obstacle. The system analyzes environmental images, identifies obstacles, and pre-calculates avoidance trajectories, enabling the robot to execute smooth corrections without speed reductions or wasted time on reactive maneuvers.
4Reliability
If the effective imaging distance is extended to capture more environmental information, then obstacle detection capability improves, but computing power consumption increases
Solution Approach 1:
The patent dynamically adjusts the effective imaging distance parameter based on ambient lighting conditions to optimize the balance between obstacle detection accuracy and computing power consumption. In bright conditions, a longer imaging distance captures more information with sufficient signal quality, while in dim conditions, the imaging distance is shortened to reduce the computational burden of processing low-quality images.
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
Improves obstacle avoidance efficiency and path control accuracy by adaptively controlling imaging distance and travel speed, reducing computing power consumption, and ensuring safe and efficient mowing operations under varying lighting conditions.
Implementation Method 1
configuring a photosensitive sensor disposed on the mowing robot to acquire forward light intensity information during the current mowing operation
Data Source
AI summary
Provided is a method for adaptive mowing control of a mowing robot, the method relates to the technical field of the mowing robot. The method includes: performing block segmentation on a real-time operation environment image into a near-field environment image portion and a far-field environment image portion based on an operation travel speed of a mowing robot and using an effective imaging distance as a segmentation parameter, wherein a maximum operation travel speed is positively proportional to an area proportion of the near-field environment image portion in the real-time operation environment image. The area proportion does not exceed 25%, and the area proportion is related to ambient light intensity. By dividing the real-time operation environment image into the near-field and the far-field environment image portions, the method reduces computing power consumption in processing the real-time operation environment image.