Mixed-Reality Robotic Control With Haptic Guidance and Path Constraints
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Solution Overview
Problem
Current robotic control methods, such as teleoperation and full automation, are limited in their ability to adapt to varied and dynamic environments, requiring either intense human operator involvement or pre-programmed AI that may not be effective in complex scenarios, and lack an intuitive human-machine interface for mixed-initiative control.
Innovation Solution
A haptic-enabled mixed reality system that uses RGBD sensors and a haptic-enabled input/output device to allow human operators to visualize and interact with a 3D scene, providing high-level guidance to robots through indirect and intermittent inputs, enabling flexible and adaptable remote control by generating a virtualized representation of the physical environment and allowing users to experience physical characteristics and modify the scene with virtual objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If teleoperation mode is used where human operator precisely controls every move of the robot in real-time, then control precision is improved, but operator burden and system complexity increase significantly
Solution Approach 1:
The control system is segmented into two distinct modes: teleoperation mode for precise real-time control and autonomous mode for automated task execution. This segmentation allows the operator to switch between modes depending on the situation, reducing overall operator burden while maintaining control precision when needed.
Solution Approach 2:
The system dynamically transitions between teleoperation and autonomous modes based on operational requirements. The robot can autonomously execute pre-programmed tasks and switch to teleoperation mode when human intervention is needed, making the control system adaptable and reducing continuous operator engagement.
2Device complexity
If full automation mode is used with pre-programmed AI control, then operator burden is reduced, but adaptability to varied operation scenarios deteriorates
Solution Approach 1:
A hybrid control architecture acts as an intermediary between full automation and teleoperation. The system uses pre-programmed autonomous control for routine tasks and automatically engages teleoperation mode when encountering scenarios requiring human judgment, thus maintaining both low operator burden and high adaptability.
Solution Approach 2:
The control system dynamically adjusts its level of autonomy based on environmental feedback and task requirements. When the robot encounters unfamiliar or complex scenarios, it transitions from autonomous mode to teleoperation mode, ensuring adaptability while maintaining reduced operator burden for routine operations.
3Device complexity
If mixed-initiative control mode is implemented where human provides high-level instructions and robot determines exact movements, then operator burden is reduced, but control precision and real-time responsiveness may deteriorate
Solution Approach 1:
The control system segments decision-making into two levels: high-level task planning by the human operator and low-level motion execution by the robot's autonomous control. This segmentation reduces operator burden while maintaining precision through the robot's capable autonomous navigation and manipulation systems.
Solution Approach 2:
The system dynamically transitions between mixed-initiative mode and full teleoperation mode based on operational needs. When precise real-time control is required, the operator can take direct control, ensuring that control precision is maintained when necessary while benefiting from reduced burden during autonomous operation.
4Ease of operation
If intuitive human-machine interface for remote mixed-initiative control is developed, then ease of operation is improved, but system complexity and development difficulty increase
Solution Approach 1:
The virtualized environment creates a digital copy of the physical workspace that the operator interacts with. This virtual representation allows for intuitive control by mapping physical actions to virtual actions, making the interface more natural while the complex processing occurs in the virtual model rather than requiring complex physical interface hardware.
Data Source
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AI summary
A virtualized environment corresponding to a physical environment currently surrounding a robot is displayed. The virtualized environment is updated in accordance with streaming environment data received from sensors collocated with the robot. A first user input inserting a first virtual object at a first location in the virtualized environment is detected. The virtualized environment is modified in accordance with the insertion of the first virtual object at the first location. The first virtual object at the first location causes the robot to execute a first navigation path in the physical environment. A second user input is detected that moves the first virtual object along a movement path to a second location in the virtualized environment. The movement path is constrained by simulated surfaces in the virtualized environment, and the first virtual object at the second location causes the robot to execute a modified navigation path in the physical environment.