System and method to virtually teach a semi-autonomous device
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of teaching semi-autonomous devices to navigate and perform tasks in target environments is time-consuming and labor-intensive, requiring skilled human operators and resulting in higher costs and reduced productivity, especially when errors or undesirable behaviors necessitate re-recording of action sequences.
Innovation Solution
A virtual teaching system that uses environmental data from sensors to construct a virtual representation of the target environment, allowing human operators to teach a virtual device an action sequence via a user interface, which can then be transferred to the semi-autonomous device for execution in the actual environment, with tools for recording, editing, and testing the sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a skilled human operator physically pilots the semi-autonomous device to teach it navigation, then the device can learn the target environment, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system creates a virtual copy of the target environment using sensor data, and a virtual device that replicates the physical semi-autonomous device. The human operator teaches the virtual device in the simulated environment, and this teaching data is then transferred to control the physical device. This copying approach eliminates the need for time-consuming physical piloting while maintaining navigation accuracy.
Solution Approach 2:
A virtual environment and virtual device serve as an intermediary between the human operator and the physical semi-autonomous device. The operator interacts with the virtual device to teach navigation paths, and the system automatically transfers this knowledge to the physical device, eliminating the need for direct physical teaching and reducing time loss.
2Reliability
If the semi-autonomous device exhibits errors during teaching, then the recorded path must be rerecorded, but this increases labor intensity and time consumption
Solution Approach 1:
By teaching the virtual device instead of the physical device, errors can be identified and corrected in the virtual environment without requiring physical rerecording. The virtual teaching process can be repeated efficiently, and only the final corrected path needs to be transferred to the physical device, significantly improving teaching efficiency while maintaining task performance reliability.
Solution Approach 2:
The system performs preliminary testing and error correction in the virtual environment before deploying the teaching data to the physical device. This preliminary action allows all necessary corrections to be made in advance, preventing the need for time-consuming rerecording operations and improving overall productivity.
3Reliability
If a skilled human operator is required to teach the device, then teaching quality is maintained, but operational costs increase
Solution Approach 1:
The virtual device and virtual environment provide a cost-effective platform that maintains teaching quality without requiring expensive skilled operators to be physically present. The virtual teaching system can be operated by less expensive personnel while still achieving high-quality teaching results, thereby reducing operational costs while maintaining reliability.
Data Source
AI summary
A virtual teaching system includes a semi-autonomous device to perform a task such as cleaning a target environment. The semi-autonomous device includes one or more sensors configured to record environmental data from the target environment that can be used to construct a virtual environment. The semi-autonomous device is operably coupled to an analysis system. The analysis system includes a processor to perform multiple functions, such as constructing the virtual environment from the recorded environmental data and supporting operation of a user interface. The user interface can be operably coupled to the processor, allowing a human operator to teach a virtual device in the virtual environment to perform an action sequence. Once the virtual device has been taught an action sequence in the virtual environment, the analysis system can transfer the recorded action sequence to the semi-autonomous device for use in the target environment.