Robot Footfall Stair Detection for Safe Stair Traversal
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Solution Overview
Problem
Robots lack the natural coordination to navigate stairs and stair-like terrain, leading to potential missteps, slips, or falls, which can be catastrophic.
Innovation Solution
A robot system that identifies stairs from footfalls by analyzing sensor data to detect patterns in foot placement, using a stair detector to generate stair models based on footfall data, and adjust movement strategies to safely traverse stairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots traverse stairs using conventional movement control, then basic locomotion is achieved, but coordination precision deteriorates leading to missteps and falls
Solution Approach 1:
The system performs preliminary detection of stair geometry and footfall patterns before actual traversal. The stair detector analyzes footfall data to identify stair characteristics in advance, allowing the robot to prepare appropriate movement strategies before encountering the stairs, thereby improving coordination and safety.
Solution Approach 2:
The system continuously monitors footfall data during traversal and uses this feedback to adjust movement control in real-time. By analyzing the pattern, timing, and location of footfalls, the system can detect when the robot is approaching stairs and modify its movement strategy accordingly, preventing missteps and falls.
2Device complexity
If robots use simple movement control for unknown terrain, then device complexity is reduced, but detection precision of stair features deteriorates
Solution Approach 1:
The robot uses its own footfall data as the sensing mechanism to detect stairs, eliminating the need for additional specialized sensors. The stair detector analyzes patterns in the robot's natural walking footfalls to infer stair geometry and characteristics, providing accurate detection without increasing device complexity.
Data Source
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AI summary
A method of identifying stairs (20) includes receiving a plurality of footfall locations (128) of a robot (100). Each respective footfall location indicates a location where a leg (120) of the robot contacted a support surface (12). The method also includes determining a plurality of candidate footfall location pairs (212) where the candidate footfall location pair includes a first and a second candidate footfall location. The method further includes clustering the first candidate footfall location into a first cluster group (222) based on a height of the first candidate footfall location and clustering the second candidate footfall location into a second cluster group based on a height of the second candidate footfall location. The method additionally includes generating a stair model (202) by representing each of the cluster groups as a corresponding stair and delineating each stair based on a respective midpoint (MP) between each adjacent cluster group.