Marker Map Monitoring for AGV Localization Changes
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Solution Overview
Problem
In warehouse environments with Automated Guided Vehicles (AGVs), the static marker setups for localization often go undetected when changes occur, requiring frequent human intervention to ensure markers are present and correctly positioned, which can lead to inefficiencies and potential navigation errors.
Innovation Solution
A method and system that continuously updates the marker setup by maintaining a map of markers with their last detection times, flagging markers not detected within a threshold time, and allowing automated addition or removal of markers, enabling robotic devices to detect changes and request human intervention or corrective actions through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static marker setups are used for AGV localization, then the system is simple to implement, but changes in marker setups go undetected requiring frequent human intervention
Solution Approach 1:
The system implements feedback by having robots continuously detect markers and report their positions to a central system. The system compares detected marker positions with the expected map configuration, automatically identifying when markers are missing, misplaced, or have moved. This closed-loop feedback mechanism eliminates the need for manual marker verification while maintaining high reliability.
Solution Approach 2:
The marker monitoring system performs self-service by automatically detecting and flagging marker setup changes without human intervention. The system uses robot detection data to self-diagnose marker issues, self-correct by updating the marker map when changes are detected, and self-monitor continuously to prevent navigation errors.
2Reliability
If frequent human checks are performed to verify marker presence and position, then marker reliability is maintained, but productivity decreases due to manual intervention
Solution Approach 1:
The system replaces manual human checks with an automated electronic monitoring system. Instead of workers physically inspecting marker positions, the system uses robot-mounted sensors to automatically detect marker positions and compares them against the expected configuration. This substitution of mechanical human inspection with electronic automation maintains reliability while eliminating productivity losses.
Solution Approach 2:
The system introduces an intermediary automated monitoring layer between the markers and human operators. Rather than direct human inspection, the intermediary system collects marker detection data from robots, processes the information, compares it with the marker map, and only alerts humans when actual changes are detected. This intermediary mechanism maintains reliability while minimizing human intervention and maximizing productivity.
3Measurement precision
If manual verification of marker setups is performed, then navigation accuracy is maintained, but time is lost to human intervention
Solution Approach 1:
The system implements continuous monitoring of marker positions through robot detections, eliminating the discontinuous nature of manual checks. Robots continuously detect markers during normal operations, and the system continuously compares detected positions with the marker map in real-time. This continuous useful action maintains high localization accuracy without any loss of time to periodic human interventions.
Solution Approach 2:
The system performs preliminary detection and verification of marker changes before they affect navigation. By continuously monitoring marker positions and comparing them against the expected configuration, the system identifies and flags marker issues proactively. This preliminary action ensures navigation accuracy is maintained by detecting problems before they cause errors, eliminating the need for reactive human time investment.
4Productivity
If automated marker detection is implemented, then productivity increases by reducing human intervention, but system complexity increases
Solution Approach 1:
The system achieves productivity gains through a universal multi-functional approach. The same robot detection infrastructure used for primary localization purposes is also utilized for marker monitoring and change detection. The central control system performs both navigation management and marker verification functions. This multi-functionality increases productivity by eliminating separate manual verification processes while avoiding the complexity of dedicated separate hardware systems.
Data Source
AI summary
Embodiments are provided that include maintaining a map of a plurality of markers in an environment. The map includes a last detection time of each marker of the plurality of markers. The embodiments also include receiving a set of detected markers from a robotic device that is configured to localize in the environment using the plurality of markers. The embodiments further include updating, in the map, the last detection time of each marker which has a mapped position that corresponds to a detected position of a detected marker in the set of detected markers. The embodiments additionally include identifying, from the plurality of markers in the map, a marker having a last detection time older than a threshold amount of time. The embodiments still further include initiating an action related to the identified marker.