Autonomous Robot Navigation With Virtual Exclusion Regions
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Solution Overview
Problem
Existing autonomous mobile robots face challenges in safely navigating around virtual exclusion regions due to limitations in existing marking systems, which can lead to unintended damage or restricted access, and require more flexible and user-friendly methods for defining and managing these regions.
Innovation Solution
The method involves using sensors and electronic maps to detect and avoid virtual exclusion regions, allowing robots to treat them similarly to real obstacles, with features like user-defined boundaries, communication for exchanging exclusion region data, and automatic recognition of risk areas to dynamically define exclusion zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical markers (lighthouse, magnet strips) are used to define exclusion regions, then the robot can reliably detect and avoid these regions, but the user's freedom to arrange living space is limited and the system becomes less flexible
Solution Approach 1:
The patent uses virtual copies of physical markers stored in the robot's memory. Instead of requiring actual physical markers in the environment, the robot creates and stores virtual representations of exclusion regions based on user input or automatic detection, then navigates using these virtual copies. This eliminates the need for physical markers while maintaining reliable exclusion region definition.
Solution Approach 2:
The patent replaces physical mechanical markers (lighthouse devices, magnet strips) with virtual electronic markers stored in memory. The robot uses sensor data and map information to define exclusion regions virtually, substituting the mechanical marker system with an electronic/software-based system that provides greater flexibility while maintaining reliability through precise sensor detection and memory storage.
2Adaptability or versatility
If virtual exclusion regions are entered directly into the map, then no additional markings are needed in the environment, but user error or measurement errors may lead to unintended damage
Solution Approach 1:
The patent implements feedback mechanisms where the robot detects obstacles using sensors, determines their positions, and uses this information to refine and verify exclusion region definitions. The system continuously compares sensor data with map information, allowing users to verify and correct virtual exclusion region boundaries before finalizing them, thereby reducing errors while maintaining flexibility.
Solution Approach 2:
The patent allows users to preliminarily define virtual exclusion regions based on initial sensor data or rough estimates, then performs verification steps where the robot tests these regions against actual sensor detections. This preliminary definition followed by verification reduces the risk of errors while maintaining the flexibility of virtual markers, as errors can be caught and corrected before the robot operates autonomously.
3Reliability
If the robot treats virtual exclusion regions the same as real obstacles, then navigation safety is improved, but the system complexity increases
Solution Approach 1:
The patent merges the treatment of virtual exclusion regions with real obstacle detection by integrating both into a unified navigation avoidance system. The robot's path planning algorithm handles both virtual markers from memory and real obstacles from sensor data using the same avoidance logic, simplifying the control system while improving navigation safety through consistent treatment of all exclusion zones.
Data Source
AI summary
A method for controlling an autonomous, mobile robot which is designed to navigate independently in a robot deployment area, using sensors and a map. According to one embodiment, the method comprises detecting obstacles and calculating the position of detected obstacles based on measurement data received by the sensors, and controlling the robot to avoid a collision with a detected obstacle, the map comprising map data that represents at least one virtual blocked region which, during the control of the robot, is taken into account in the same way as an actual, detected obstacle.