Multi-Robot Persistent Map Sharing for Coordinated Cleaning Coverage
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Solution Overview
Problem
Existing mobile cleaning robots operate independently, lacking a unified and persistent map of their environment, which hinders efficient task coordination and coverage, especially when multiple robots are involved, as they each create their own maps without sharing information effectively.
Innovation Solution
A system of mobile cleaning robots equipped with local storage and sensors to create and share a persistent map, allowing them to navigate, coordinate, and perform cleaning tasks together, with one robot flagging dirty areas for another to focus on, utilizing a central server for map updates and coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If each mobile cleaning robot operates independently and creates its own map, then the robot can autonomously navigate and clean, but multiple robots cannot coordinate effectively and coverage is redundant or incomplete
Solution Approach 1:
Multiple mobile cleaning robots merge their individual maps into a single shared persistent map stored on a server. Each robot contributes its sensed environment data, and the server consolidates this information into a unified map that all robots can access, eliminating redundancy and improving coordination.
Solution Approach 2:
A server acts as an intermediary between multiple mobile cleaning robots, receiving map data from each robot, maintaining the persistent shared map, and distributing it back to the robots. This mediator enables information exchange without requiring direct robot-to-robot communication.
2Productivity
If multiple robots clean simultaneously without coordinated mapping, then cleaning can occur in parallel, but robots may collide or clean the same areas repeatedly
Solution Approach 1:
Robots perform preliminary mapping and share the persistent map before beginning parallel cleaning operations. This advance preparation allows each robot to understand the environment and coordinate with others, preventing collisions and redundant cleaning while maintaining parallel operation efficiency.
Solution Approach 2:
The system implements feedback loops where robots continuously update the shared persistent map with new environmental information and receive updates from the server. This real-time feedback enables dynamic coordination during parallel cleaning operations, allowing robots to adjust their paths and avoid conflicts.
3Measurement precision
If robots generate new maps for each cleaning task, then the mapping is fresh and accurate, but the system loses historical environment information and requires repeated mapping
Solution Approach 1:
The persistent map on the server continuously accumulates and maintains environmental information across multiple cleaning tasks and robots. Instead of discarding map data after each task, the system preserves and builds upon historical mapping information, eliminating the need for repeated full mapping while maintaining accuracy through incremental updates.
Data Source
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AI summary
A multi-robot system includes a first a mobile cleaning robot that has a local storage device to store a persistent map of an environment, at least one sensor to sense the environment, and a control module. The control module is configured to: control the mobile cleaning robot to navigate in the environment using the persistent map and sensing data provided by the at least one sensor, share the persistent map with a second mobile cleaning robot, and coordinate with the second mobile cleaning robot to perform cleaning tasks.