Constrained Mobility Mapping for Real-Time Robot Step Planning
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Solution Overview
Problem
Robotic devices face challenges in navigating constrained environments without colliding with obstacles while maintaining balance and speed, leading to inefficient and arduous movement.
Innovation Solution
The method involves generating voxel maps and spherical depth maps using sensor data to identify obstacles and ground voxels, updating the voxel map to reflect changes, and creating body obstacle, ground height, and no step maps to guide the robot's movement, ensuring obstacle avoidance and balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time navigation and step planning is implemented to avoid obstacles, then collision avoidance is improved, but navigation speed and movement efficiency deteriorate
Solution Approach 1:
The navigation system is divided into multiple specialized maps (voxel map for obstacle detection, spherical depth map for distance measurement, body obstacle map for collision avoidance, ground height map for terrain adaptation, no step map for safe zone identification). Each map handles a specific aspect of navigation, allowing parallel processing and real-time updates without compromising overall navigation speed.
Solution Approach 2:
The voxel map and associated navigation maps are dynamically updated in real-time as the robot moves through the environment. Sensor data continuously refreshes the obstacle positions and terrain information, enabling the robot to adapt its path planning on-the-fly while maintaining high navigation speed through efficient incremental updates rather than complete recalculation.
2Measurement precision
If complex voxel mapping and real-time updates are performed, then obstacle detection accuracy is improved, but computational complexity and processing time worsen
Solution Approach 1:
Multiple sensor data streams and processing functions are merged into an integrated voxel mapping system. The voxel map consolidates obstacle information from various sensors into a unified three-dimensional representation, reducing computational overhead by processing all spatial data through a single coherent framework rather than multiple separate algorithms.
Solution Approach 2:
The system creates simplified two-dimensional projections (body obstacle map, ground height map, no step map) from the three-dimensional voxel map. These copied representations maintain essential obstacle information in formats optimized for specific navigation tasks, reducing computational complexity for real-time decision-making while preserving detection accuracy.
3Manufacturing precision
If multiple specialized maps (body obstacle map, ground height map, no step map) are generated, then navigation accuracy and balance are improved, but data processing time and system complexity worsen
Solution Approach 1:
The system pre-generates multiple specialized maps from the voxel map data structure before navigation decisions are required. The body obstacle map, ground height map, and no step map are prepared in advance with optimized data formats for their specific purposes, allowing rapid retrieval and use during real-time navigation without intensive processing at the moment of decision-making.
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.