Robot Direction Estimation Using Particle Filter Weights
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Solution Overview
Problem
Conventional moving robot control systems face challenges in accurately estimating robot direction due to external disturbances and non-Gaussian probability distributions in sensor data, leading to erroneous direction estimation and movement.
Innovation Solution
A control system that uses a necessary condition to determine the validity of sensor data, calculates weights based on whether the data satisfy this condition, and employs a particle filter to estimate the robot's direction, even when data are affected by external magnetic field interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a predetermined rule is used to determine whether disturbance can be ignored, then the data selecting part can operate, but sensing data with non-Gaussian distribution may be incorrectly accepted leading to erroneous direction estimation
Solution Approach 1:
The patent replaces the mechanical rule-based data selection system with a probabilistic model (particle filter) that can handle non-Gaussian distributions. Instead of using fixed thresholds or simple consistency checks, the system uses probability density functions to evaluate the likelihood that sensor data represents true robot direction, enabling accurate estimation even when data exhibits complex statistical properties that defy simple rule-based filtering
Solution Approach 2:
The patent changes the fundamental parameter used for data validation from binary rule satisfaction to continuous probability values. By transforming the data selection criterion from a deterministic rule (pass/fail) to a probabilistic measure (likelihood ratio), the system can gracefully handle uncertain and non-Gaussian sensor data, adjusting the acceptance threshold based on the computed probability rather than applying rigid predetermined rules
2Device complexity
If simple rule-based filtering is applied to sensor data, then processing is simple, but the system cannot accurately distinguish between valid data and disturbed data under non-Gaussian distributions
Solution Approach 1:
The patent replaces simple rule-based filtering with a probabilistic inference mechanism using particle filters. Instead of applying predetermined rules that compare sensor readings against fixed thresholds or consistency criteria, the system computes the probability that each sensor reading corresponds to the true robot direction by comparing it against a probability distribution model, thereby reliably distinguishing valid from disturbed data even under non-Gaussian conditions
Solution Approach 2:
The patent introduces an intermediary probabilistic model (the particle filter representation of direction probability distribution) that mediates between raw sensor data and final direction estimation. This intermediary layer transforms uncertain, potentially disturbed sensor readings into a refined probability distribution that reflects the true direction, effectively filtering out disturbances without requiring complex rule-based validation at each processing stage
Data Source
AI summary
A moving robot apparatus and a control system and method thereof. A current state variable (e.g., the current direction of the main body of the robot apparatus) may be estimated using current data output from a sensor part of the robot apparatus. Initially, it is determined whether the current data output from the sensor part satisfy a necessary condition. The necessary condition may be considered to be satisfied when the current data are unaffected by a disturbance or external interference (e.g., the earth's magnetic field.). Thereafter, a current sample value may be extracted from a previous sample value obtained from the sensor part. The previous sample value may relate to a previous state variable (e.g., a previous direction of the robot main body). A weight of the current data may be calculated based on the current sample value. A value of the weight may depend on whether the necessary condition is satisfied by the current data. The current state variable is then estimated based on the extracted current sample value and the calculated weight. A control of the directional movement of the robot main body may be provided based on the estimated current state variable. A control system may be provided to accurately estimate, using a probability distribution, the direction of the robot main body when it can not be determined whether or not an external disturbance is reflected in the data output from the sensor part.