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 voxel maps and spherical depth maps using sensor data to identify obstacles and update the environment representation, allowing for the creation of body obstacle maps, ground height maps, and no step maps that guide the robot's movement to avoid collisions and maintain balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation methods are used in constrained environments, then the robot can maintain simple processing, but the robot experiences slow movement and frequent collisions
Solution Approach 1:
The patent segments the environment representation into multiple specialized maps: voxel maps for 3D spatial occupancy, spherical depth maps for distance information, body obstacle maps for robot-specific clearance, ground height maps for terrain elevation, and no step maps for prohibited areas. This segmentation allows each map to specialize in specific navigation aspects, enabling real-time processing while maintaining high reliability in collision avoidance.
Solution Approach 2:
The patent transitions from 2D navigation to 3D navigation by implementing voxel maps that represent environmental occupancy in three-dimensional space. This dimensional enhancement allows the robot to perceive and navigate complex constrained environments more accurately, improving both movement speed and collision avoidance by utilizing vertical space information.
2Reliability
If real-time sensor processing is implemented for obstacle detection, then collision avoidance improves, but computational complexity increases
Solution Approach 1:
The patent divides the complex processing task into separate modules: voxel map generation handles 3D occupancy, spherical depth map generation handles distance calculation, and specialized map generation (body obstacle, ground height, no step) handles specific navigation requirements. This modular segmentation reduces computational complexity by allowing each module to process specific aspects independently and efficiently.
Solution Approach 2:
The patent performs preliminary processing by continuously maintaining updated voxel maps and spherical depth maps from sensor data before navigation decisions are required. This preliminary action pre-computes environmental representations, so when real-time navigation decisions are needed, the robot can query pre-processed map data rather than processing raw sensor data from scratch, reducing real-time computational complexity.
Data Source
AI summary
A method of constrained mobility mapping includes receiving from at least one sensor of a robot at least one original set of sensor data and a current set of sensor data. Here, each of the at least one original set of sensor data and the current set of sensor data corresponds to an environment about the robot. The method further includes generating a voxel map including a plurality of voxels 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 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.