Robot Navigation Using Digital Twins and Virtual Sensors
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Solution Overview
Problem
Autonomous robots face challenges in adapting to complex and unstructured environments, particularly with dynamic obstacles, changing terrains, and ambiguous scenarios, leading to inefficiencies and increased operational downtime due to manual intervention for navigation and decision-making.
Innovation Solution
Utilizing simulated environments, such as digital twins, to replicate real-world conditions in real-time, incorporating additional virtual sensors to enhance navigation and decision-making capabilities, allowing for real-time path planning and rescue strategies without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous robots navigate complex and unstructured environments using traditional sensor data and predefined maps, then basic navigation is achieved, but the robots struggle with dynamic obstacles, changing terrains, and ambiguous scenarios leading to operational downtime
Solution Approach 1:
The system creates a digital twin of the environment in advance and uses simulated sensors to pre-process and enhance navigation data before the real robot operates. This preliminary simulation allows the robot to prepare navigation strategies for complex scenarios without real-time delays.
Solution Approach 2:
The patent creates a virtual copy (digital twin) of the real environment and robot, complete with simulated sensors that replicate and enhance the capabilities of physical sensors. This copying allows testing and enhancement of navigation algorithms in a risk-free virtual space before deployment to the real robot.
2Ease of operation
If additional virtual sensors are incorporated in the simulated environment, then navigation and decision-making capabilities are enhanced, but the complexity of the system increases
Solution Approach 1:
The digital twin acts as an intermediary between the real robot and the complex environment. Simulated sensors in the digital twin process and enhance navigation data, then translate this enhanced information back to the real robot, effectively mediating the complexity rather than directly increasing it.
Solution Approach 2:
By copying the sensor suite into the virtual environment, the system enhances navigation capabilities without adding physical sensors to the real robot. The virtual sensors provide additional data streams that improve decision-making without increasing physical device complexity.
3Productivity
If real-time simulation is used to provide navigation support, then manual intervention is reduced, but computational resources and processing time are increased
Solution Approach 1:
The system performs preliminary simulations to pre-compute navigation paths and potential obstacles before the robot needs to act. By preparing navigation strategies in advance through simulation, the real-time computational burden on the actual robot is reduced, improving operational efficiency without excessive energy consumption during execution.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques that train and use trained language models to assist users with complex systems installation, troubleshooting, and/or maintenance. A method can include determining, responsive to data received from a real robot having one or more real sensors and operating in a real environment, that the real robot needs assistance to navigate from a current state of the real robot within the real environment, causing simulated data to be obtained from one or more simulated sensors within a simulated environment at least partially modeling the real environment, the one or more simulated sensors including at least one simulated sensor different from the one or more real sensors, and using the simulated data to control operation of the real robot within the real environment in order to navigate the real robot from the current state.