Robot Arm Teaching with Gesture Control and VIO Collision Avoidance
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Solution Overview
Problem
Existing hand gesture control methods for teaching robotic arms are limited by operator perception, leading to inefficient and potentially unsafe trajectories due to the need for constant monitoring of both hand gestures and environmental surroundings, and lack of autonomous collision avoidance.
Innovation Solution
Integrate visual-inertial-odometry (VIO) using a second sensor system with a tracking camera and IMU on the robotic arm to detect obstacles and autonomously correct the trajectory, combining it with hand gesture control to ensure a collision-free and optimized path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hand gesture control is used to teach robotic arm, then ease of operation is improved, but reliability deteriorates due to operator perception limitations and potential collisions
Solution Approach 1:
The system implements feedback by equipping the robotic arm with sensors (cameras, LIDAR, force-torque sensors) that continuously monitor the environment and provide real-time information about obstacles and potential collisions. This feedback loop allows the control system to adjust the taught trajectory autonomously, preventing collisions while maintaining the simplicity of hand gesture control for the operator.
Solution Approach 2:
The robotic arm performs self-service by autonomously detecting obstacles and correcting its own trajectory without requiring operator intervention. The onboard sensors and processing systems enable the arm to independently assess environmental hazards and modify its path, thereby maintaining reliability while preserving the ease of operation provided by intuitive gesture control.
2Reliability
If operator constantly monitors both hand gestures and environmental surroundings, then reliability is improved, but productivity deteriorates due to constant switching of attention
Solution Approach 1:
The system assigns the monitoring task to the robotic arm itself through its onboard sensors and processing systems. The arm autonomously tracks environmental surroundings, detects obstacles, and adjusts its trajectory without requiring the operator to divide attention between gesture control and environmental monitoring. This self-service capability maintains reliability while preserving operator productivity.
Solution Approach 2:
The control system acts as an intermediary between the operator's hand gestures and the robotic arm's movement. It receives simple gesture commands from the operator and automatically integrates environmental sensor data to compute safe trajectories, eliminating the need for the operator to directly monitor surroundings while maintaining collision avoidance.
3Reliability
If visual-inertial-odometry is integrated with hand gesture control, then reliability is improved through autonomous collision avoidance, but device complexity increases
Solution Approach 1:
The robotic arm is designed with multi-functionality, serving both as the manipulated object and as the active sensing platform. The same robotic structure that executes tasks also carries sensors and performs environmental monitoring, eliminating the need for separate fixed monitoring infrastructure and reducing overall system complexity while improving reliability.
Solution Approach 2:
The system merges the control input method (hand gestures) with the sensing system (visual-inertial-odometry) into a unified control framework. The gesture recognition and VIO systems are integrated at the software level, allowing seamless combination of operator intent with autonomous environmental awareness, thereby achieving improved reliability without proportionally increasing hardware complexity.
Data Source
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.