Stair Tracker Edge Verification for Accurate Robot Step Fitting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robots face challenges in traversing staircases due to the complexity of sensor data interpretation, particularly during stair ascent and descent, which can lead to inaccurate detection of edges and risers, resulting in potential missteps and safety issues.
Innovation Solution
A stair tracking system that processes sensor data to detect and track features of stairs by comparing data over time, using a detection tracker to verify the accuracy of detected edges and generating a staircase model with defined edges and heights, thereby improving the robot's ability to navigate stairs safely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot uses sensor data to detect stair edges, then the robot can identify stair features for navigation, but the complexity of sensor data interpretation leads to inaccurate detection of edges and risers
Solution Approach 1:
The patent segments the sensor data processing into distinct modules: edge detection module, riser detection module, and verification module. Each module handles specific aspects of stair feature detection independently, reducing the complexity of overall data interpretation while maintaining detection accuracy through specialized processing for each feature type.
Solution Approach 2:
The patent implements a feedback mechanism where detected edges and risers are verified by comparing sensor data across multiple time steps and comparing detected features against the generated staircase model. This feedback loop allows the system to correct detection errors and refine measurements, improving accuracy despite the complexity of sensor data.
2Measurement precision
If the robot processes sensor data over multiple time steps to verify edge detection, then the accuracy of stair feature detection improves, but the time required for navigation increases
Solution Approach 1:
The patent performs preliminary edge detection at each time step before full verification, allowing the system to quickly identify potential features and then apply more time-consuming verification only to these candidates. This preliminary action reduces the overall time required while maintaining accuracy by focusing computational resources on likely features.
Solution Approach 2:
The patent applies partial verification by comparing detected edges against the staircase model only for critical features or when confidence thresholds are not met. For high-confidence detections, full multi-time-step verification is skipped, reducing time loss while maintaining sufficient accuracy for safe navigation.
3Manufacturing precision
If the robot generates a detailed staircase model with defined edges and heights, then the robot achieves precise leg movement and foot placement, but the complexity of data processing and model generation increases
Solution Approach 1:
The patent applies local quality by generating detailed staircase model information only where needed for precise foot placement, rather than creating a complete detailed model of the entire staircase. The system focuses computational resources on defining edges and heights at specific locations where the robot will step, reducing overall data processing complexity while maintaining foot placement precision.
Solution Approach 2:
The patent extracts only the essential features needed for navigation and foot placement from the complete sensor data, rather than processing and storing all available information. By taking out only the critical edge and height measurements required for safe traversal, the system achieves precise foot placement with reduced data processing complexity.
Data Source
AI summary
A method for perception and fitting for a stair tracker includes receiving sensor data for a robot adjacent to a staircase. For each stair of the staircase, the method includes detecting, at a first time step, an edge of a respective stair of the staircase based on the sensor data. The method also includes determining whether the detected edge is a most likely step edge candidate by comparing the detected edge from the first time step to an alternative detected edge at a second time step, the second time step occurring after the first time step. When the detected edge is the most likely step edge candidate, the method includes defining, by the data processing hardware, a height of the respective stair based on sensor data height about the detected edge. The method also includes generating a staircase model including stairs with respective edges at the respective defined heights.