Autonomous Robot Navigation with Topological Maps and Local Constraints
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Solution Overview
Problem
Existing autonomous navigation systems for robots, particularly in complex urban environments, rely on dense a priori mapping which is delicate and sensitive to changes in the environment, often requiring costly and bulky sensors like LiDAR.
Innovation Solution
A method and system for autonomous navigation that utilizes topological mapping and local domain restrictions, employing sensors such as monocular cameras, IMU, and RTK GPS to create a simplified topological map, allowing the robot to navigate based on a network of interconnected landmarks and pathways, constraining motion locally to ensure safety and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense a priori mapping with LiDAR is used for autonomous navigation, then localization precision is improved, but device cost and size increase
Solution Approach 1:
The patent extracts only the essential navigational elements from the environment (landmarks and pathways) rather than creating a complete dense map. This selective extraction allows the robot to navigate effectively using simplified topological representations, reducing the need for bulky LiDAR sensors while maintaining navigation capability.
Solution Approach 2:
The patent replaces expensive, permanent dense mapping infrastructure with cheaper, temporary topological mappings that are created on-demand during robot operation. This approach uses computationally lightweight representations that can be generated and discarded as the robot moves through the environment, reducing hardware costs.
2Measurement precision
If dense mapping is used for navigation, then navigation accuracy is improved, but adaptability to environmental changes deteriorates
Solution Approach 1:
The patent implements dynamic topological mapping where the environmental representation is continuously updated and adapted as the robot encounters new landmarks or pathways. This dynamic approach allows the system to adjust to environmental changes in real-time, improving both accuracy and adaptability compared to static dense maps.
Solution Approach 2:
The patent focuses computational resources on creating accurate topological representations only in the robot's local field of view and immediate pathway, rather than maintaining high-fidelity maps of the entire environment. This local quality approach reduces sensitivity to distant environmental changes while maintaining navigation accuracy in the robot's operational zone.
3Device complexity
If topological mapping with local constraints is used, then device cost is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent introduces topological landmarks and pathways as intermediary representations between the robot's sensors and its navigation decisions. These intermediaries provide a computationally efficient framework that maintains localization precision without requiring expensive direct sensing of every environmental detail, bridging the gap between low-cost sensors and accurate navigation.
Data Source
AI summary
A system and method of autonomously navigating an autonomous robot is described. The described method involves creating a topological mapping of an area of environment around a location of the mobile robot; identifying at least one pathway around the location; and locally constraining a motion of the mobile robot based on the topological mapping and the identified at least pathway. The method expects inaccuracies in the localization to happen within an acceptable range and mitigates these errors by locally constraining the motion of the robot or vehicle to what is defined safe upon an analysis of data perceived through one or more robot sensors.