Simulated Robot Environments for Dynamic Obstacle Navigation
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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 reliance on 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 perception and navigation, and employing machine learning models trained on simulated data to improve robot operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous robots operate in complex and unstructured environments using traditional navigation methods, then they can execute tasks autonomously, but they struggle with dynamic obstacles, changing terrains, and ambiguous scenarios leading to navigation failures
Solution Approach 1:
The system creates a digital twin of the environment in advance and performs simulated navigation trials before actual robot deployment. Multiple virtual navigation attempts are executed in the simulated environment to identify successful paths and strategies, which are then transferred to the real robot, enabling it to handle complex and dynamic scenarios more reliably
Solution Approach 2:
A digital twin (virtual copy) of the real environment is created and maintained. This copy includes virtual sensors, virtual robots, and virtual representations of obstacles and terrains. The virtual environment replicates real-world conditions, allowing the robot to practice navigation in a safe, controllable setting before applying learned strategies in the physical world
2Measurement precision
If additional sensors are added to improve robot perception in complex environments, then navigation capability improves, but device complexity and cost increase
Solution Approach 1:
Instead of adding physical sensors to the real robot, the system creates a digital twin equipped with virtual sensors that replicate the sensing capabilities. These virtual sensors in the simulated environment provide enhanced perception data for training and planning without requiring physical hardware modifications to the actual robot
Solution Approach 2:
The system pre-configures the digital twin with additional virtual sensors and performs perception training in the simulated environment before deployment. This allows the robot to learn from enhanced sensory data in simulation, improving its environmental understanding without physically installing complex sensor arrays on the real robot
3Reliability
If manual intervention is used to help robots navigate difficult scenarios, then navigation success rate improves, but operational efficiency decreases due to increased human involvement
Solution Approach 1:
The system enables robots to self-train and self-improve by executing multiple navigation trials in the simulated environment. The virtual robot autonomously learns from successful and failed attempts, developing navigation strategies without human intervention. This self-learning capability transfers to the real robot, maintaining high success rates while eliminating the need for manual assistance
Solution Approach 2:
The system performs preliminary navigation training and strategy development in the digital twin before the real robot encounters difficult scenarios. By pre-testing and optimizing navigation approaches in simulation, the robot is better prepared to handle complex situations autonomously, reducing the need for manual intervention during actual operations
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 generating, for a real environment including a real robot having one or more real sensors, a simulated environment modeling the real environment, the simulated environment including a simulated robot corresponding to the real robot, the simulated robot including one or more simulated sensors corresponding to the one or more real sensors, obtaining simulated data based at least on simulated sensor data collected using the one or more simulated sensors, and using the simulated data to control operation of the real robot within the real environment.