Robotic Mower Object Classification for Adaptive Safety Distance
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Solution Overview
Problem
Conventional lawn mowers lack effective mechanisms to differentiate between fixed and temporary objects within their operating area, leading to potential collisions and inefficient operation.
Innovation Solution
A robotic mower equipped with an onboard positioning module and object detection module that uses sensors to determine the location of encountered objects and classify them as known or unknown, allowing for safe and efficient navigation by maintaining different safety distances based on object type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional lawn mowers operate without object classification capability, then the device complexity is low, but the reliability is reduced due to inability to differentiate between fixed and temporary objects
Solution Approach 1:
The system performs preliminary action by pre-defining the locations of fixed objects in the operating area before the mowing operation begins. The processor is configured to identify fixed objects based on their predefined locations, allowing the mower to proactively plan its path around these objects rather than reacting to them during operation. This preliminary classification improves reliability by enabling the mower to distinguish between fixed objects (which can be safely approached) and temporary objects (which should be avoided).
Solution Approach 2:
The processor acts as an intermediary between the sensor detections and the mower's navigation system. It receives sensor data, cross-references it with predefined fixed object locations, and generates classification information that guides the mower's path planning. This intermediary processing layer enables intelligent differentiation between fixed and temporary objects without requiring complex sensor systems, thus improving reliability while maintaining reasonable device complexity.
2Reliability
If the robotic mower maintains a large safety distance from all detected objects, then the reliability of avoiding collisions with temporary objects is improved, but the productivity decreases due to reduced mowing coverage
Solution Approach 1:
The system applies local quality by implementing different safety distance rules for different types of objects. The processor determines the type of object (fixed or temporary) and applies corresponding safety distance parameters: a first safety distance for fixed objects and a second, larger safety distance for temporary objects. This localized differentiation allows the mower to maintain close proximity to fixed objects (improving productivity) while keeping safe distances from temporary objects (maintaining reliability).
Solution Approach 2:
The system changes the safety distance parameter dynamically based on object classification. When a fixed object is identified, the safety distance parameter is set to a smaller value, allowing the mower to operate closer and increase mowing coverage. When a temporary object is detected, the safety distance parameter increases to a larger value to ensure collision avoidance. This parameter adaptation resolves the contradiction between reliability and productivity by optimizing the safety distance for each specific situation.
3Loss of information
If the robotic mower uses basic obstacle detection without classification, then the device complexity is low, but the loss of information occurs regarding the nature and type of detected objects
Solution Approach 1:
The system eliminates information loss by performing preliminary action: fixed object locations are predefined and stored in the system before operation begins. When sensors detect an object, the processor immediately cross-references it with the predefined locations to determine if it is a fixed object. This preliminary preparation ensures that complete object type information is retained without requiring complex real-time analysis, thus reducing device complexity while maintaining full information retention.
Data Source
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AI summary
A method for processing object detection-related information may include receiving information indicative of an encounter between a robotic mower and an object responsive to communication received from a sensor of the robotic mower while the robotic mower transits a parcel, determining a location of the robotic mower at a time corresponding to occurrence of the encounter, determining whether the location corresponds to a location associated with a known object associated with the parcel, and classifying the object as an unknown object based on the location not corresponding to the location associated with the known object.