Mobile Robot Map Alignment Using a Common Reference Frame
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Solution Overview
Problem
Mobile robots operating in the same environment face challenges in coordinating their movements and sharing data due to differences in their sensing capabilities and individual maps, leading to difficulties in interoperability, collision avoidance, and efficiency maximization.
Innovation Solution
The development of systems and methods for determining map transformations between mobile robots, allowing them to operate within a common reference frame, which includes a follower robot autonomously following a target robot through a series of tasks and analyzing position and trajectory data to synchronize map data, enabling a master control system to manage all robots effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different mobile robots use their own individual maps based on their sensing capabilities, then each robot can navigate independently, but the robots cannot coordinate their movements and share data effectively
Solution Approach 1:
The patent introduces a master control system as an intermediary that receives map data from multiple mobile robots and generates a composite reference map. This mediator coordinates the individual robot maps by aligning them through identified common locations, enabling effective data sharing and coordination while preserving each robot's independent navigation capability
Solution Approach 2:
The composite reference map serves as a universal coordinate system that all mobile robots can use for positioning and coordination. This multi-functional map structure allows the system to simultaneously support individual robot navigation and fleet-wide coordination, resolving the contradiction between independent operation and collaborative work
2Measurement precision
If manual methods are used to determine map transformations between robots, then accuracy can be achieved, but the process is time-consuming and requires human involvement
Solution Approach 1:
The system enables mobile robots to autonomously participate in map transformation determination by providing their individual map data and identified common locations to the master control system. The automated processing of this data by the master control system achieves accurate transformations without requiring manual intervention, thus resolving the contradiction between precision and time consumption
Solution Approach 2:
The method performs preliminary actions by having robots pre-identify common locations in their environments and share this information with the master control system. This preparatory data collection enables automated and accurate map transformation calculation, eliminating the need for time-consuming manual surveying while maintaining high precision
Data Source
AI summary
Systems and methods for determining map transformations between mobile robots are described. In some examples, a follower robot follows a target robot through a series of goal locations. The position and trajectory information of the follower robot can be recorded while the follower robot follows the target robot through the plurality of goal locations. Once complete, the compiled position and trajectory data can be analyzed and transformed into a common reference frame, which relates a map of the target robot with a map of the follower robot.