Mobile Robot Route Switching Under Localization Uncertainty
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Solution Overview
Problem
Existing methods for guiding mobile robots along predetermined paths connected by nodes do not effectively optimize route changes based on localization uncertainty and effort, leading to suboptimal accuracy and speed in navigation.
Innovation Solution
A method where a mobile robot automatically switches from an initial route to a replacement route if the quality function value for the replacement route is smaller, considering a weighted sum of effort and localization uncertainty, with the option to include loop routes to reduce uncertainty and optimize path efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot follows the initial route containing all remaining paths, then the robot can cover all areas, but the localization uncertainty increases and navigation accuracy deteriorates
Solution Approach 1:
The robot performs preliminary actions by traversing loop routes before completing the remaining paths. These loop routes are pre-planned to reduce localization uncertainty at critical nodes, ensuring accurate positioning before proceeding with the remaining route coverage.
Solution Approach 2:
The route planning is dynamic and adaptive. The system continuously updates the remaining route based on current localization uncertainty, robot position, and environmental factors. The quality function dynamically evaluates different route options and selects the optimal path that balances coverage efficiency with localization accuracy.
2Measurement precision
If the robot traverses loop routes to reduce localization uncertainty, then navigation accuracy improves, but the additional effort and time required increases
Solution Approach 1:
The system changes the routing parameters dynamically by introducing a quality function that incorporates localization uncertainty as a key parameter. This quality function evaluates potential routes based on multiple factors including remaining path coverage, loop route benefits for uncertainty reduction, and energy consumption, allowing the robot to select routes that optimize the balance between localization precision and energy expenditure.
3Productivity
If the robot uses a quality function to select optimal routes, then navigation efficiency improves, but the computational complexity increases
Solution Approach 1:
The quality function evaluation is performed locally at decision points rather than globally for the entire route. When the robot reaches a node, it evaluates only the immediate next steps and nearby loop routes using the quality function, rather than recalculating the entire remaining route. This local evaluation approach maintains navigation efficiency while reducing computational complexity.
Data Source
Figure 1~2
AI summary
The invention relates to a method for travelling down a prescribed arrangement of paths (K1 - K2, K2 - K3, K1 - K5), which are connected to one another at nodes (K1,...,K10), with a mobile, in particular autonomous, robot (1), wherein the robot changes (S60) from an initial route, which contains all as yet untravelled paths, to a different replacement route, which has a loop route, which retakes at least one path after taking this path and at least one further path, and a subsequent remaining route, which contains all as yet untravelled 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, wherein 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; wherein the first effort is the lower of a minimum effort for a loop route that retakes at least one path after taking this path and at least one further path and a minimum effort for a target route that contains all as yet untravelled paths; and the second effort is a minimum effort for a remaining route that contains all as yet untravelled paths after this loop route; and the variable weighting weights the second effort lower for a first localization uncertainty of the robot than for a lower second localization uncertainty of the robot, in particular weights it to the maximum, in particular identically to the first effort, for a minimum localization uncertainty of the robot and/or weights it to the minimum, in particular hides it, for a maximum localization uncertainty of the robot.