Stair Tracking Maps for Collision-Aware Robot Foot Placement
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Solution Overview
Problem
Robots face challenges in traversing environments with stairs due to the need for precise leg movement and foot placement, as they lack natural coordination and often encounter issues with poor sensor data, leading to potential collisions or damage.
Innovation Solution
A method for stair tracking that involves receiving sensor data, generating maps including ground height and movement limitations, merging these with a stair model to create an enhanced stair map, and controlling the robot to navigate stairs safely by identifying legal regions and avoiding obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot uses sensor data to generate maps and control movement, then navigation capability is improved, but collision risk increases due to poor sensor data quality
Solution Approach 1:
The patent merges multiple data sources including sensor data, pre-built maps, and real-time environmental perception to create a comprehensive navigation system. This combination allows the robot to cross-validate information and maintain reliable navigation even when individual sensors provide poor quality data, thereby reducing collision risk while preserving navigation capability.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is constantly compared against pre-built maps and movement predictions. When discrepancies are detected, the system adjusts its navigation decisions in real-time, allowing it to adapt to poor sensor conditions while maintaining safe operation through ongoing verification against expected environmental features.
2Reliability
If the robot constrains movement to avoid obstacles, then safety is improved, but movement efficiency deteriorates
Solution Approach 1:
The patent applies movement constraints selectively rather than globally. By identifying specific illegal regions and hazardous zones, the system constrains movement only where necessary for safety, while allowing full movement freedom in safe areas. This localized approach maintains safety without unnecessarily reducing overall movement efficiency.
Solution Approach 2:
The system dynamically adjusts movement constraints based on real-time conditions and confidence levels in sensor data. When sensor quality is high and environmental understanding is confident, constraints are relaxed to improve efficiency. When uncertainty increases or hazards are detected, constraints are tightened to maintain safety, creating a dynamic balance between safety and efficiency.
3Measurement precision
If the robot processes sensor data to generate detailed maps, then terrain understanding is improved, but computational complexity increases
Solution Approach 1:
The patent pre-processes and structures environmental data into organized map representations before the robot begins navigation. By preparing terrain models, obstacle databases, and movement constraint maps in advance, the system reduces the computational burden during real-time operation, allowing detailed terrain understanding without excessive real-time processing complexity.
Solution Approach 2:
The navigation system divides the environment into discrete manageable components such as voxels, terrain patches, and obstacle regions. This segmentation allows the robot to process and understand complex terrains by handling smaller discrete elements independently, reducing overall computational complexity while maintaining detailed terrain understanding through aggregation of these segmented representations.
Data Source
AI summary
A method for a stair tracking for modeled and perceived terrain includes receiving, at data processing hardware, sensor data about an environment of a robot. The method also includes generating, by the data processing hardware, a set of maps based on voxels corresponding to the received sensor data. The set of maps includes a ground height map and a map of movement limitations for the robot. The map of movement limitations identifies illegal regions within the environment that the robot should avoid entering. The method further includes generating a stair model for a set of stairs within the environment based on the sensor data, merging the stair model and the map of movement limitations to generate an enhanced stair map, and controlling the robot based on the enhanced stair map or the ground height map to traverse the environment.