Constrained Mobility Mapping for Legged Robot Obstacle Updates
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Solution Overview
Problem
Legged robotic devices face challenges in navigating constrained environments due to difficulties in real-time obstacle avoidance and maintaining balance, often resulting in slow and arduous movement with frequent collisions.
Innovation Solution
The method involves generating a voxel map from sensor data to differentiate between ground and obstacle voxels, creating a spherical depth map to detect changes, and updating the map to reflect these changes, while also producing a body obstacle map to guide the robot's movement without interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time sensor data processing is implemented for obstacle detection, then navigation speed is improved, but computational complexity increases
Solution Approach 1:
The environment is divided into discrete voxels (volume elements) that can be independently processed and updated. This segmentation allows the system to handle complex 3D spatial data in manageable units, enabling real-time processing while maintaining navigation speed.
Solution Approach 2:
The patent transitions from traditional 2D depth maps to a spherical depth map representation that incorporates three-dimensional spatial information. This dimensional enhancement allows for more comprehensive obstacle detection in constrained environments while maintaining computational efficiency through the voxel-based structure.
2Measurement precision
If detailed voxel maps are generated for precise obstacle identification, then measurement precision is improved, but data processing time increases
Solution Approach 1:
The system pre-processes sensor data into a voxel map structure that organizes spatial information in advance. This preliminary organization of data into discrete volumetric elements enables faster query and update operations during real-time navigation, reducing processing time while maintaining precision.
Solution Approach 2:
The patent employs dynamic updates to the voxel map based on changing sensor data, adjusting the representation of obstacles as new information becomes available. This parameter-based update mechanism allows the system to maintain high measurement precision without reprocessing the entire map, thus reducing data processing time.
3Measurement precision
If spherical depth maps are used for comprehensive environment mapping, then obstacle detection accuracy is improved, but device complexity increases
Solution Approach 1:
The spherical depth map structure serves multiple functions simultaneously: it provides comprehensive 360-degree environment mapping, enables accurate obstacle detection, and facilitates real-time updates through the voxel representation. This multi-functional design reduces the need for separate systems while maintaining detection accuracy.
Solution Approach 2:
The patent creates a virtual spherical representation of the physical environment through the depth map and voxel structure. This digital copy allows the robot to analyze and plan navigation paths in the virtual model without physical interaction, reducing the complexity of real-time physical sensing while maintaining detection accuracy.
4Reliability
If frequent map updates are performed for real-time navigation, then navigation reliability is improved, but energy consumption increases
Solution Approach 1:
The system performs map updates at periodic intervals or triggered by significant changes in sensor data, rather than continuously. This periodic update mechanism maintains navigation reliability by ensuring the map reflects current environmental conditions while reducing energy consumption compared to continuous updating.
Solution Approach 2:
The patent extracts only the essential changes from sensor data for map updates, focusing on detecting and representing obstacle changes rather than processing all sensor information. This selective extraction approach maintains navigation reliability by capturing critical environmental changes while minimizing the computational energy required for updates.
Data Source
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AI summary
A method of constrained mobility mapping includes receiving from at least one sensor (132) of a robot (100) at least one original set of data (134) and a current set of data from an environment (10) about the robot (100). The method further includes generating a voxel map (210) including a plurality of voxels (212) based on the at least one original set of sensor data. The plurality of voxels includes at least one ground voxel and at least one obstacle voxel. The method also includes generating a spherical depth map (218) based on the current set of sensor data and determining that a change has occurred to an obstacle represented by the voxel map based on a comparison between the voxel map and the spherical depth map. The method additional includes updating the voxel map to reflect the change to the obstacle.