Mobile Robot Route Switching for Localization-Uncertain Path Networks
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Solution Overview
Problem
Existing methods for autonomous mobile robots traveling along prescribed paths connected at nodes do not effectively optimize route accuracy and velocity, particularly in environments where localization uncertainty increases due to unknown terrain.
Innovation Solution
A method and system that allow the robot to automatically switch from an initial route to a replacement route if the quality function of the replacement route is lower, based on a weighted sum of efforts considering loop routes and remaining untraveled paths, with variable weighting dependent on localization uncertainty, to reduce uncertainty and optimize route efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot follows the initial route containing all untraveled paths, then the robot can systematically cover the environment, but localization uncertainty increases due to unknown terrain
Solution Approach 1:
The system continuously monitors localization uncertainty and uses this feedback to dynamically adjust the route planning. When uncertainty exceeds a threshold, the system triggers recalculation of the initial route and generation of replacement routes, creating a closed-loop control system that adapts to changing localization conditions
Solution Approach 2:
The quality function uses variable weighting parameters that change based on localization uncertainty. As uncertainty increases, the weighting of different route characteristics (such as loop routes vs. direct paths) changes, allowing the system to prioritize routes that reduce uncertainty when needed
2Reliability
If the robot takes loop routes to reduce localization uncertainty, then accuracy improves, but the route length and traversal time increase
Solution Approach 1:
The route planning is dynamic rather than static. The system continuously evaluates both the initial route and replacement routes based on current localization uncertainty, allowing the robot to adapt its path in real-time. This dynamic approach ensures that loop routes are only taken when necessary to reduce uncertainty below acceptable thresholds
Solution Approach 2:
The system takes partial loop routes only when and where needed to reduce localization uncertainty, rather than implementing complete loop routes throughout the entire traversal. This partial action approach minimizes the additional route length while still achieving the necessary uncertainty reduction
3Productivity
If the robot frequently recalculates and switches routes based on quality function values, then route optimization improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary calculations of multiple replacement routes in advance, before they are actually needed. By pre-computing potential alternative routes and their quality function values, the system reduces the computational burden during real-time operation when quick decisions are necessary
Solution Approach 2:
The system calculates only a limited number of replacement routes (e.g., top k routes) rather than evaluating all possible routes. This partial calculation approach provides sufficient optimization without the excessive computational complexity of exhaustive search
Data Source
AI summary
A method for traveling down a prescribed arrangement of paths which are connected to one another at nodes with a mobile robot. The robot changes from an initial route, which contains all as yet untraveled paths, to a different replacement route including a loop route which retakes at least one path and at least one further path, and a subsequent remaining route which contains all as yet untraveled paths at that time if a value of a quality function for the replacement route is lower than a value of this quality function for the initial route. The quality function is dependent on a first effort, a second effort, and a variable weighting of the first and second values in relation to one another. The variable weighting weights the second effort lower for a first localization uncertainty of the robot.