Robot Arm Teaching With Hand Gestures and Collision-Free VIO Paths
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Solution Overview
Problem
Existing hand gesture control methods for teaching robotic arms are limited by the operator's perception, leading to potentially unsafe and inefficient trajectory teaching, as the operator may not fully account for obstacles in the environment.
Innovation Solution
The method combines hand gesture control with visual-inertial-odometry (VIO) to detect impending collisions and automatically adjust the robotic arm's trajectory to avoid obstacles, ensuring a collision-free path while allowing the operator to continue guiding the arm with hand gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hand gesture control is used to teach robotic arm trajectory, then operator acceptance and ease of operation are improved, but trajectory safety and reliability deteriorate due to limited operator perception
Solution Approach 1:
The system implements real-time feedback by detecting the robotic arm's surrounding environment during teaching and providing this information back to the control device. The processor compares the intended trajectory with detected obstacles and provides feedback adjustments, allowing the operator to see potential collision risks and adjust hand gestures accordingly, thus maintaining ease of operation while improving trajectory safety
Solution Approach 2:
The system introduces an intermediary environmental detection and analysis layer between the operator's hand gestures and the robotic arm execution. This intermediary process detects obstacles, analyzes trajectory safety, and automatically adjusts or alerts the operator about potential collisions, mediating between the simple hand gesture input and the safe trajectory execution without adding complexity to the operator's interface
2Adaptability or versatility
If operator manually teaches trajectory using hand gestures, then adaptability to different tasks is improved, but productivity deteriorates due to inefficient and unsafe trajectories
Solution Approach 1:
The system performs preliminary environmental detection and trajectory analysis before the robotic arm executes the taught movement. By detecting obstacles and analyzing potential collisions in advance, the system can pre-adjust the trajectory or alert the operator before execution, preventing unsafe movements and reducing the need for repetitive trial-and-error teaching, thus improving productivity while maintaining adaptability
Solution Approach 2:
The system provides self-service by automatically detecting the environment, analyzing trajectory safety, and making necessary adjustments without requiring constant operator intervention. The robotic arm teaching process becomes partially autonomous, where the system serves itself by identifying and correcting potential trajectory issues, reducing teaching time and improving productivity while preserving the operator's ability to adapt to different tasks
Data Source
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AI summary
A computer-implemented method of teaching a robot system is provided. The method may comprise detecting, by a first sensor system (102) of the robot system, an operator's hand gesture (103), and moving (S3) a robotic arm (101) of the robot system in accordance with the operator's hand gesture (103). The method may comprise detecting (S31), by a second sensor system (104) of the robot system, an impending collision of the robotic arm (101) with an obstacle (105), and moving the robotic arm (101) along at least a section of a collision- free path (106) around the obstacle (105). By advantageously combining hand gesture control and visual-inertial-odometry, the invention results in an improved teaching of a robotic arm trajectory.