Predictive Sensor Reconfiguration for Motion-Blurred Robotics
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Solution Overview
Problem
Robotic devices face challenges in capturing clear sensory signals due to movement, leading to blurry images and inaccurate sensor readings, which affect navigation and data accuracy.
Innovation Solution
A method and system that dynamically adjust sensor configurations based on predicted future sensor readings and navigation path data to optimize sensor parameters and movement configurations, improving signal quality by reconfiguring sensors and motion controls in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the robotic device moves to navigate the environment, then the robotic device can collect sensory data from different locations, but the movement causes blurry images and inaccurate sensor readings
Solution Approach 1:
The system performs preliminary actions by predicting future sensor readings based on navigation path data before the robotic device actually moves to those positions. This allows the control system to pre-adjust sensor configurations (such as exposure time, gain, or sampling rate) to optimal values for the anticipated conditions, thereby preventing image blur and sensor inaccuracies before they occur during movement
Solution Approach 2:
The system dynamically adjusts sensor configurations in real-time based on predicted future readings and actual movement conditions. The sensor parameters are not fixed but are continuously optimized according to the robotic device's current speed, acceleration, and anticipated trajectory, allowing the system to adapt sensor settings to match varying movement conditions and maintain high data quality
2Measurement precision
If the sensor configuration is adjusted to optimize signal capture, then the quality of sensory signals improves, but the system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where actual sensor readings are continuously compared with predicted future sensor readings. Based on this feedback, the control system automatically adjusts sensor configurations to minimize the difference between predicted and actual readings, thereby optimizing measurement precision without requiring complex manual configuration. The feedback loop enables automatic adaptation to varying conditions
Solution Approach 2:
The sensor configuration system serves itself by automatically determining optimal settings based on navigation path data and predicted sensor readings. Rather than requiring external intervention or complex manual tuning, the system self-adjusts sensor parameters (such as exposure time, gain, or sampling frequency) to optimize signal capture quality, reducing the operational complexity for users
Data Source
AI summary
Systems and methods for optimizing sensory signal capturing by reconfiguring robotic device configurations. A method includes determining at least one predicted future sensor reading for a robotic device based on navigation path data of the robotic device, wherein the robotic device is deployed with at least one sensor, wherein each predicted future sensor reading is an expected value of a future sensory signal; determining an optimized sensor configuration based on the at least one predicted future sensor reading, wherein the optimized sensor configuration optimizes capturing of sensor signals by the at least one sensor; and reconfiguring the at least one sensor based on the optimized sensor configuration, wherein reconfiguring the at least one sensor further comprises modifying at least one sensor parameter of the at least one sensor based on the optimized sensor configuration.