18062300. LIFELONG ROBOT LEARNING FOR MOBILE ROBOTS simplified abstract (Robert Bosch GmbH)
Contents
- 1 LIFELONG ROBOT LEARNING FOR MOBILE ROBOTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LIFELONG ROBOT LEARNING FOR MOBILE ROBOTS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Mobile Robot Performance Optimization Method
- 1.13 Original Abstract Submitted
LIFELONG ROBOT LEARNING FOR MOBILE ROBOTS
Organization Name
Inventor(s)
Katsu Yamane of Mountain View CA (US)
Sharath Gopal of Fremont CA (US)
Alexander Kleiner of Neuhausen (DE)
Robert Schirmer of Schoemberg (DE)
LIFELONG ROBOT LEARNING FOR MOBILE ROBOTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18062300 titled 'LIFELONG ROBOT LEARNING FOR MOBILE ROBOTS
Simplified Explanation
The patent application describes a method for enhancing the performance of a mobile robot in completing tasks in an environment by updating a database/model with recorded sensor data and modifying the robot's operating procedure based on this information.
- The method involves receiving data recorded by sensors on the mobile robot as it navigates the environment.
- This data is used to update a database/model associated with the environment.
- The operating procedure of the robot is then adjusted based on the updated database/model to improve its performance.
- Recommendations for further enhancing the robot's performance can also be generated and displayed to the user.
Key Features and Innovation
- Utilizes recorded sensor data to update a database/model for the environment.
- Modifies the operating procedure of the mobile robot based on the updated information.
- Provides recommendations for improving the robot's performance in completing tasks.
Potential Applications
The technology can be applied in various industries such as manufacturing, logistics, and healthcare where mobile robots are used to perform tasks in dynamic environments.
Problems Solved
- Enhances the performance of mobile robots in completing tasks in different environments.
- Provides a systematic approach to improving the efficiency and effectiveness of mobile robot operations.
Benefits
- Increased efficiency and accuracy in task completion by mobile robots.
- Enhanced adaptability of robots to changing environmental conditions.
- Improved user experience through personalized recommendations for performance enhancement.
Commercial Applications
- "Enhanced Mobile Robot Performance Optimization Method" can be utilized in warehouse automation, hospital logistics, and smart manufacturing processes to streamline operations and improve productivity.
Prior Art
Research on mobile robot navigation and performance optimization in various environments can provide insights into similar methods and technologies.
Frequently Updated Research
Stay updated on advancements in sensor technology, artificial intelligence, and robotics to further enhance the capabilities of mobile robots in dynamic environments.
Questions about Mobile Robot Performance Optimization Method
How does the method improve the performance of mobile robots in completing tasks?
The method enhances performance by updating a database/model with recorded sensor data and modifying the robot's operating procedure based on this information.
What industries can benefit from this technology?
Industries such as manufacturing, logistics, and healthcare can benefit from the enhanced performance and efficiency of mobile robots in completing tasks.
Original Abstract Submitted
A method is disclosed for improving a mobile robot that is configured to perform a task in an environment using an operating procedure. Data is received that was recorded by the mobile robot using one or more sensors as the mobile robot navigates the environment to perform the task. A database and/or a model associated with the environment is updated to incorporate the recorded data. The operating procedure of the mobile robot can be modified, based on the database and/or the model, to generate a modified operating procedure for performing the task in the environment that improves a performance of the mobile robot. Additionally, a recommendation for improving the performance of the mobile robot when performing the task in the environment can be determined, based on the database and/or the model, and displayed to a user for consideration.
