Thermal Guidance for Agricultural Vehicle Obstacle Avoidance
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Solution Overview
Problem
Agricultural operations pose a threat to vulnerable juvenile animals like deer fawns during mowing, and static obstacles can damage equipment and hinder efficiency, necessitating a systematic approach to safeguard wildlife and ensure safe operation.
Innovation Solution
A guidance system for agricultural vehicles using thermal cameras and machine learning to detect heat signatures, generate geofences, and adjust vehicle operations to avoid obstacles and animals, incorporating GNSS and IMU data for precise navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If thermal cameras and machine learning are used to detect heat signatures, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the detection task by using thermal cameras to capture heat signatures and machine learning algorithms to analyze and classify them. This divides the complex detection problem into manageable components: thermal image capture, heat signature identification, and object classification, thereby improving detection accuracy while keeping each component's complexity controlled.
Solution Approach 2:
The patent introduces an intermediary processing layer between the thermal camera and the decision-making system. Machine learning models act as intermediaries that process raw thermal data, extract meaningful patterns, and provide structured output for navigation adjustments. This intermediary layer enhances detection accuracy while abstracting the complexity from the overall system control.
2Reliability
If the agricultural vehicle stops and reverses to avoid objects, then safety is improved, but productivity decreases
Solution Approach 1:
The system dynamically adjusts the vehicle's operation based on real-time detection. Instead of predetermined stopping points, the vehicle continuously monitors its environment and adjusts its path, speed, and stopping decisions dynamically. This allows the vehicle to maintain high productivity by only stopping when necessary while ensuring safety through continuous monitoring and adaptive response.
Solution Approach 2:
The system changes operational parameters (speed, direction, stopping distance) based on detected object characteristics. For less critical objects, the vehicle may reduce speed and navigate around them without stopping. For critical objects, it performs controlled stops and reversals. This parameter adaptation optimizes the balance between safety and productivity by matching the response intensity to the detected risk level.
3Reliability
If geofences are generated around detected objects, then object protection is improved, but navigation complexity increases
Solution Approach 1:
The system performs preliminary actions by generating geofences around detected objects before the vehicle reaches them. These geofences serve as pre-planned protective zones that guide the vehicle's navigation in advance. By establishing these virtual boundaries beforehand, the system ensures object protection is in place before any potential collision risk arises, while the navigation system simply needs to avoid these pre-defined zones.
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
Prevents wildlife casualties and equipment damage by accurately detecting and navigating around obstacles and animals, ensuring safe and efficient agricultural operations.
Implementation Method 1
receive image data from an image sensor, analyze the received image data to identify heat signatures represented in the received image data
Data Source
AI summary
A guidance system for controlling operation of an agricultural vehicle includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the guidance system, during an agricultural process, to receive image data from an image sensor, analyze the received image data to identify heat signatures represented in the received image data, analyze the identified heat signatures to determine a presence and a type of an object, generate an alert indicating the presence of the object, and output the alert.