Walking-Support Robot Mode Switching from Handlebar Load Trends
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Solution Overview
Problem
Existing walking-support robots face challenges in providing comfortable assistance to users with varying physical abilities, as they struggle to accurately switch between guidance and manual modes based on handlebar load tendencies, leading to inappropriate mode changes and reduced user comfort.
Innovation Solution
A walking-support robot equipped with a detection unit, a moving device, and a switching unit that corrects handlebar load values using load tendency data generated from past movements, allowing for precise mode switching based on user-specific physical abilities and movement intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot switches support mode based on detected handlebar load, then the robot can adapt to user movement intentions, but the mode switching becomes inaccurate when the user has varying physical abilities and load tendencies
Solution Approach 1:
The robot performs preliminary actions by collecting handlebar load data during guidance mode operation before mode switching is needed. This preliminary data collection allows the robot to establish user-specific load tendencies and thresholds in advance, enabling more accurate mode switching judgments when actually needed.
Solution Approach 2:
The robot implements feedback mechanisms by continuously monitoring handlebar load and comparing it against dynamically adjusted thresholds. The system uses feedback from past interactions to refine its understanding of user load tendencies, improving the accuracy of mode switching decisions over time through adaptive threshold adjustment.
2Measurement precision
If the robot collects and processes load tendency data from past movements, then the mode switching accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The robot applies local quality by implementing targeted data processing rather than comprehensive analysis. It focuses computational resources on specific aspects of load data relevant to mode switching decisions, such as directional load components and threshold comparisons, rather than processing all possible movement parameters.
Solution Approach 2:
The system manages complexity through parameter changes by dynamically adjusting decision thresholds based on collected data. Instead of increasing computational algorithms, the robot modifies the parameters (threshold values) used in relatively simple comparison operations, achieving improved accuracy through parameter adaptation rather than computational complexity.
Data Source
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AI summary
A robot includes a main body, a handlebar disposed on the main body and grippable by a user, a detection unit that detects a load applied to the handlebar, a moving device including a rotating body and moving the robot by controlling the rotation of the rotating body, and a switching unit that switches a support mode for supporting the user with walking. The support mode includes a first mode in which the robot autonomously moves to guide the user who is walking and a second mode in which the robot moves in accordance with a first load detected by the detection unit. When the robot moves in the first mode, the switching unit switches the support mode from the first mode to the second mode on the basis of the second load detected by the detection unit.