Robotic Mower Collision Detection with Adaptive Motor Current Prediction
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Solution Overview
Problem
Existing self-propelled robotic tools, such as robotic lawnmowers, face challenges in reliable collision detection due to varying environmental conditions and tool aging, leading to false or missed collision detections.
Innovation Solution
The method involves updating prediction algorithm parameters based on current errors exceeding thresholds, allowing the algorithm to adapt to specific circumstances, including grass type and weather conditions, using sensors like GPS and IMU data to improve collision detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a prediction algorithm with fixed parameters is used for collision detection, then the device complexity is low, but the reliability of collision detection deteriorates due to false detections in varying environmental conditions
Solution Approach 1:
The prediction algorithm parameters are transformed from static fixed values to dynamic adaptive values that automatically adjust based on real-time operating conditions. The system continuously monitors current errors and updates parameters to match varying environmental conditions such as grass density, terrain, and weather, thereby maintaining high collision detection reliability without requiring manual intervention or complex configuration.
Solution Approach 2:
A feedback mechanism is implemented where the system continuously compares actual motor currents with predicted currents, calculates current errors, and uses this error information to update prediction algorithm parameters. This closed-loop feedback ensures that the algorithm adapts to changing conditions and maintains accurate collision detection, resolving the contradiction between simplicity and reliability.
2Reliability
If the prediction algorithm parameters are updated frequently to adapt to changing conditions, then the reliability improves, but the loss of time increases due to repeated parameter updates
Solution Approach 1:
The parameter update process is organized as a periodic operation that occurs at predetermined time intervals rather than continuously. This periodic updating mechanism balances the need for adaptability with the constraint of processing time, allowing the system to maintain reliable collision detection while minimizing the time spent on parameter updates by performing them at optimal intervals.
3Adaptability or versatility
If the robotic tool operates in varying environmental conditions such as different grass types and weather, then the adaptability increases, but the measurement precision of collision detection deteriorates due to false positives
Solution Approach 1:
The system dynamically changes prediction algorithm parameters based on detected operating conditions such as grass density, terrain characteristics, and weather conditions. By adjusting parameters like motor current predictions and error thresholds to match current environmental conditions, the system maintains precise collision detection across diverse environments, preventing false positives while preserving adaptability.
Data Source
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AI summary
The present disclosure relates to a self-propelled robotic work tool (1), e.g. an automatic robotic lawn mower, and a corresponding method. The robotic tool comprises an inertia measurement unit (IMU 15) which generally obtains (25) measured IMU parameters regarding the robotic working tool's movement. A prediction algorithm (17) predicts (27,36) required motor currents for driving the robotic work tool's wheels (5) based on the measured IMU parameters. The predicted motor current is compared (29,37) to the actual current used and the difference constitutes an error (19), which is used in a collision detection unit (21). If the collision detection unit (21) senses that the actually used motor current is much higher than the predicted current, a collision may be indicated (31). The prediction algorithm is repeatedly updated based on the error (19) by incrementing or decrementing an error category counter (error cat, 41,45) if the error (19) is above or bellow a first or second threshold (39,43), and increasing or decreasing a prediction algorithm setting (49,53), e.g. a motor current offset term i offset- if the error category counter (error cat, 41,45) is above or bellow a third or fourth threshold (47,51). This allows the prediction algorithm to adapt to circumstances where the robotic tool is used. For instance, if a lawn mower operates in thick grass, the prediction algorithm can be adapted not to detect false collisions due to increased motor current values.