Robot State Estimation From Conductive Flexible Touch Material
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Solution Overview
Problem
Existing robot systems require numerous special sensors at side surfaces to detect changes in state, leading to increased size and operational impediments, and lack flexibility in adapting to individual user interactions.
Innovation Solution
Utilize a conductive flexible material with changing electrical characteristics to estimate robot state information, operation information, and user identification without special detection equipment, employing a learning model trained on time series electrical characteristics to infer these states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerous special sensors are provided at side surfaces to detect changes in state, then detection capability is improved, but robot size increases and operations are impeded
Solution Approach 1:
The patent replaces mechanical touch sensors with an image processing system that uses a camera to detect touch positions. The camera captures images of the robot's side surface, and touch positions are identified by analyzing changes in the captured images, eliminating the need for physical sensors at multiple locations.
Solution Approach 2:
The patent creates a virtual model (image data) of the robot's side surface and uses this copied representation to detect touch positions. Instead of physically sensing touches at multiple points, the system captures visual information and processes it to identify touch locations, reducing hardware requirements.
2Measurement precision
If numerous special sensors are provided at side surfaces to detect changes in state, then detection capability is improved, but operational smoothness deteriorates
Solution Approach 1:
The patent replaces mechanical sensors with an optical detection system using a camera. This substitution eliminates physical sensor arrays that could impede robot movements, allowing smoother operations while maintaining detection capability through image processing.
3Measurement precision
If sensors are disposed at all portions of the robot to detect touches, then touch detection coverage is improved, but device complexity increases
Solution Approach 1:
The patent makes the camera system universal by using it to detect touches at any position on the robot's side surface, rather than requiring position-specific sensors. The image processing algorithm can identify touch locations anywhere in the camera's field of view, providing comprehensive coverage with a single detection device.
Solution Approach 2:
The patent replaces a complex array of distributed sensors with a single camera system. The image processing capability allows this single device to detect touches across the entire side surface, dramatically reducing device complexity while maintaining comprehensive detection coverage.
4Measurement precision
If a separate device is required for identifying users, then user identification accuracy is improved, but robot size increases
Solution Approach 1:
The patent makes the camera system multi-functional by using it for both touch detection and user identification. The same imaging device that captures touch positions also captures user characteristics, eliminating the need for separate identification hardware and reducing robot size.
Solution Approach 2:
The patent merges touch detection and user identification functions into a single camera-based system. By combining these functions, the robot achieves comprehensive capability without the size increase that would result from adding separate identification devices.
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
Enables efficient estimation of robot states and user interactions without enlarging the robot's size, reducing the need for multiple sensors, and allowing adaptive operation based on user-specific touch states.
Implementation Method 1
the flexible material including conductivity, and an electrical characteristic of the flexible material changing in response to a change in applied pressure
Data Source
AI summary
A flexible material provided at a robot is conductive and an electrical characteristic of the flexible material changes in response to a change of state. The electrical characteristic between plural detection points of the robot is detected by a detection unit. An estimation unit uses a learning model to estimate a robot state from the electrical characteristic of the robot. The learning model is trained so as to input the electrical characteristic and output the robot state. The learning model is trained using, as training data, electrical characteristics when changes of state occur at the flexible material and robot states after the changes of state of the flexible material of the robot. The estimation unit inputs the electrical characteristic to the learning model and outputs the robot state corresponding to the inputted electrical characteristic.