Lifelong robot learning for mobile robots
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Solution Overview
Problem
Current robot vacuum systems do not effectively utilize trajectory data from previous cleaning missions to improve performance or efficiency in future missions and fail to associate camera images with sensor data for navigation enhancements.
Innovation Solution
A mobile robot system that records and accumulates data from multiple missions, using this information to modify its operating procedure and provide recommendations for improving performance, such as setting 'no-go' zones, adjusting trajectory planning, and identifying problematic objects or areas, to enhance efficiency and performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robot vacuum systems collect and store trajectory data from previous cleaning missions, then navigation performance and cleaning efficiency can be improved over time, but device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing trajectory data, camera images, and sensor data from previous cleaning missions in databases. This preliminary data accumulation enables the robot to learn from past experiences and improve navigation performance in future missions without adding complexity to the core cleaning function.
Solution Approach 2:
The system implements feedback mechanisms by analyzing stored trajectory and sensor data to generate recommendations for improving cleaning performance. The feedback loop allows the robot to continuously learn from past missions, identify problematic areas, and optimize future navigation paths, thereby improving productivity without proportionally increasing device complexity.
2Reliability
If the system modifies operating procedures based on accumulated data, then performance improves over time, but the complexity of the control system increases
Solution Approach 1:
The control system is designed to be dynamic and adaptive, modifying operating procedures based on accumulated data from previous missions. The system can dynamically adjust navigation paths, identify no-go zones, and optimize cleaning patterns without requiring a completely complex redesign of the control architecture, thereby improving reliability while managing complexity.
Solution Approach 2:
The system provides self-service capabilities by automatically analyzing its own trajectory and sensor data to generate performance improvements. The robot can autonomously learn from its experiences and adjust its operating procedures without external intervention, improving navigation performance while minimizing the need for complex external control systems.
3Loss of information
If camera images are associated with sensor data and trajectory information, then environmental understanding improves, but data storage and processing requirements increase
Solution Approach 1:
The system merges camera images with sensor data and trajectory information into integrated environmental models. By combining these different data types, the system achieves comprehensive environmental understanding without storing all raw data separately, thereby reducing overall storage requirements while maintaining information completeness.
Solution Approach 2:
The system extracts key environmental features and characteristics from the combined camera and sensor data, storing only the essential information needed for navigation and cleaning optimization. This extraction approach maintains environmental information completeness while significantly reducing data storage requirements by eliminating redundant data.
Data Source
AI summary
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.