Robot Task Area Control With Iterative Image Feedback
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Solution Overview
Problem
Existing robot control systems struggle to efficiently navigate and perform tasks in unknown environments without relying on strict models, particularly when sensor sensitivity is low or calibration errors occur.
Innovation Solution
A robot control system that utilizes active sensing and considers both environment recognition and potential for action to iteratively extract and approach a task area, using image sensors to calculate probabilities and adjust robot movement based on these calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot uses traditional sensor-based environment recognition, then it can detect objects in the environment, but it fails to accurately identify task areas when sensor sensitivity is low or calibration errors occur
Solution Approach 1:
The system employs iterative feedback by repeatedly extracting task areas from images and adjusting robot movement based on calculated probabilities. The robot acquires images, calculates task area probabilities, extracts task areas, moves toward them, and repeats the process, continuously refining its identification accuracy despite sensor limitations
Solution Approach 2:
The system performs preliminary task area extraction and probability calculation before the robot physically moves. By pre-identifying potential task areas and calculating their probabilities in advance, the system prepares accurate target information that guides subsequent robot movement, compensating for sensor inaccuracies
2Manufacturing precision
If the robot moves directly to a detected target without iterative refinement, then the navigation is simple and fast, but the task execution precision decreases in dynamic environments
Solution Approach 1:
The system dynamically adjusts the task area extraction process by repeatedly acquiring new images and recalculating probabilities as the robot moves. This dynamic iterative approach allows the system to adapt to changing environmental conditions and maintain high task execution precision throughout the navigation process
Solution Approach 2:
The iterative process maintains continuous useful action by constantly extracting task areas and calculating probabilities throughout the robot's movement. Rather than performing a single extraction and then moving, the system continuously refines its task area identification, ensuring accurate task execution while navigating
3Adaptability or versatility
If the system extracts task area once and moves robot directly, then the control process is simple, but it cannot adapt to changes in dynamic environments
Solution Approach 1:
The system employs periodic action by repeatedly extracting task areas at regular intervals during robot movement. The robot acquires images, extracts task areas, moves toward them, and repeats this periodic cycle, allowing continuous adaptation to environmental changes through structured repeated measurements
Solution Approach 2:
By performing preliminary task area extraction and probability calculation before each movement phase, the system prepares adaptive guidance information in advance. This preliminary action enables the robot to respond to environmental changes systematically without requiring complex real-time decision-making during movement
Data Source
AI summary
A robot control system includes circuitry to iteratively move a robot toward a task area in which the robot is to perform a task on a target object, by acquiring a first image of an observation area in a vicinity of the robot from an image sensor, calculating a probability that the observation area includes the task area based on the first image, extracting the task area from the first image based on the probability, controlling the robot to cause the robot to approach the task area, acquiring a second image of the observation area from the image sensor, after the robot approaches the task area, calculating a probability that the observation area includes the task area based on the second image, extracting the task area from the second image based on the probability, and controlling the robot to further approach the task area extracted from the second image.