Sensor Fusion for Machine Pose Control
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Solution Overview
Problem
Conventional machine pose determination systems, such as those using GPS and LIDAR, fail to provide accurate estimates during periods of GPS signal unavailability or unreliability due to errors like multipath errors and position jumps, lacking real-time accuracy and reliability.
Innovation Solution
A method and system utilizing Inertial Measurement Units (IMUs) and non-IMU sensors, with Kalman filter modules to fuse acceleration and angular rate measurements, estimate joint angles, and solve kinematic equations for real-time position, velocity, and acceleration values, improving machine footing, stability, and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS and LIDAR systems are used to determine machine position, then position information can be obtained, but accuracy deteriorates during GPS signal unavailability or unreliability
Solution Approach 1:
The patent combines multiple sensors (GPS, LIDAR, IMU, odometer, wheel encoders) into a unified sensor fusion system that integrates their data through algorithms like Kalman filtering. This merging allows the system to maintain reliable position estimation by compensating for individual sensor weaknesses - particularly using IMU and odometer data to maintain accuracy when GPS signals are unavailable or erroneous.
2Measurement precision
If multiple sensors are integrated for sensor fusion, then measurement accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the sensor fusion system into distinct functional modules: GPS receiver, LIDAR sensor, IMU, odometer, wheel encoder, and separate processing algorithms. Each sensor and processing function is treated as an independent unit that can be individually calibrated, maintained, and replaced without affecting the entire system, thereby managing complexity while maintaining measurement precision.
3Speed
If real-time position data is processed continuously, then operational responsiveness improves, but computational energy consumption increases
Solution Approach 1:
The patent implements periodic sensor activation and data processing cycles rather than continuous operation. Sensors and processing algorithms operate in discrete time steps, updating position estimates at optimized intervals based on machine state and operational requirements. This periodic approach maintains real-time responsiveness while significantly reducing computational energy consumption compared to continuous processing.
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
The system provides accurate, real-time machine state information, enhancing operational precision, reliability, and efficiency by integrating IMU and non-IMU sensor data, even in conditions where GPS signals are unreliable, thereby improving machine control and performance.
Implementation Method 1
The signals from each IMU are then fused with a separate Kalman filter module of the at least one processor
Data Source
AI summary
Controlling machine pose using sensor fusion includes receiving from each of a plurality of Inertial Measurement Units mounted on different components, a time series of signals indicative of acceleration and angular rate of motion for each of the components of the machine, and from at least one non-IMU sensor, a signal indicative of at least one of position, velocity, or acceleration of at least one of the components, a position, velocity, or acceleration of any potential obstacles or other features, or an operator input. The signals are fused with a separate Kalman filter module to estimate an output joint angle. Estimated and measured values of the output joint angle in successive timesteps are combined, a kinematic equation is solved to determine a real time value for at least one of position, velocity, and acceleration of the component at successive timesteps, and the determined real time value is applied in an implementation of a controlled operational movement of the machine component in a successive time step.