Adaptive Search Region Sensing for Agricultural Obstacle Detection
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Solution Overview
Problem
Current agricultural machines lack an efficient method to optimize obstacle detection and navigation in varying environments, leading to suboptimal performance in different agricultural areas.
Innovation Solution
A sensing system equipped with sensors like LiDAR and cameras that adjust the search region pattern based on the agricultural machine's location, using data from GNSS and environment maps to enhance obstacle detection and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed search region pattern is used for obstacle detection, then the system structure is simple, but the detection accuracy and adaptability to different environments deteriorate
Solution Approach 1:
The patent implements dynamic adjustment of search region patterns based on the agricultural machine's location. The search region generation unit changes the pattern of search regions according to location information from GNSS receivers, transforming a static detection system into a dynamic one that adapts to different environments, thereby improving detection accuracy without requiring complex hardware modifications
Solution Approach 2:
The system changes parameters of the search region pattern (such as region boundaries, search intensity, or detection thresholds) based on location data. By modifying these parameters dynamically according to the agricultural machine's position in different field areas, the system optimizes obstacle detection accuracy for each specific location without increasing device complexity
2Adaptability or versatility
If the search region pattern is adjusted according to location, then the adaptability to different environments is improved, but the system complexity increases
Solution Approach 1:
The search region generation unit serves multiple functions: it generates search regions, adjusts patterns based on location, and adapts to different environmental conditions. This multi-functional component achieves environmental adaptability without requiring separate specialized systems for each function, thereby limiting the increase in overall system complexity
Solution Approach 2:
The search region generation unit acts as an intermediary between the GNSS location data and the obstacle detection sensors. It processes location information and translates it into appropriate search region patterns, mediating between the positioning system and detection system without requiring direct complex integration between them
3Productivity
If a uniform search region pattern is used across all areas, then the system is easy to operate, but the detection efficiency in varying environments deteriorates
Solution Approach 1:
The system performs self-adjustment of search region patterns based on its own location data. The search region generation unit automatically modifies detection parameters according to GNSS position information without requiring manual intervention or complex operator decisions, thereby improving detection efficiency while maintaining operational simplicity
Solution Approach 2:
The system uses feedback from GNSS location data to continuously adjust the search region pattern. This closed-loop approach where location information feeds back into detection parameter adjustment enables the system to optimize detection efficiency for each environment automatically, without requiring complex manual operation
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
This approach improves the accuracy and efficiency of obstacle detection and navigation, allowing the agricultural machine to adapt to different environments and perform tasks more effectively.
Implementation Method 1
sensors like LiDAR and cameras that adjust the search region pattern
Data Source
AI summary
A sensing system for a mobile agricultural machine includes one or more sensors to sense an environment around the agricultural machine to output sensing data, and a processor configured or programmed to detect an object in a search region around the agricultural machine based on the sensing data and to change a pattern of the search region for the detection of the object in accordance with an area in which the agricultural machine is located.