Robotic Arm Obstacle Avoidance via Depth Camera Vision
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Solution Overview
Problem
Existing mechanical arm designs require multiple sensors and tedious manual corrections for automatic control, making them inefficient in navigating around obstacles during medical procedures.
Innovation Solution
An automatic control system that uses a depth camera to obtain color images and depth information, processes these images to generate depth images, and employs an environmental image recognition module to output displacement coordinates, allowing the mechanical arm to move autonomously and avoid obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If multiple sensors are added to the mechanical arm for automatic control, then the automatic control capability is improved, but the device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical sensors with a vision-based system using a depth camera and neural network image recognition module. This substitution eliminates the need for multiple physical sensors on the mechanical arm while achieving automatic obstacle detection and avoidance through image processing and coordinate transformation algorithms.
2Measurement precision
If manual correction operations are performed during each operation, then the accuracy of movement is improved, but the productivity decreases
Solution Approach 1:
The system implements self-service automatic control by using the depth camera to capture environmental images, the neural network module to recognize obstacles and generate coordinate data, and the control module to automatically adjust the mechanical arm's movement. This closed-loop system eliminates the need for manual correction operations while maintaining accurate obstacle avoidance and path navigation.
3Difficulty of detecting and measuring
If image processing and recognition modules are implemented, then the obstacle detection capability is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary neural network image recognition module that processes depth camera images and converts them into mechanical arm control coordinates. This intermediary layer translates visual information into actionable control data, enabling obstacle detection and avoidance without requiring direct complex sensing on the mechanical arm itself.
Data Source
AI summary
An automatic control method of a mechanical arm and an automatic control system are provided. The automatic control method includes the following steps: obtaining a color image and depth information corresponding to the color image through a depth camera; performing image space cutting processing and image rotation processing according to the color image and the depth information to generate a plurality of depth images; inputting the depth images into an environmental image recognition module such that the environmental image recognition module outputs a displacement coordinate parameter; and outputting the displacement coordinate parameter to a mechanical arm control module such that the mechanical arm control module controls the mechanical arm to move according to the displacement coordinate parameter.