3D Robot Path Planning Using Depth-Grid Obstacle Mapping
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Solution Overview
Problem
Current robotic path planning methods face challenges in accurately modeling complex obstacles in dynamic environments, such as operating rooms, and are computationally inefficient when using large datasets from depth sensors like the Microsoft Azure Kinect.
Innovation Solution
The method transforms depth data from depth sensors into a robot coordinate system, discretizes the workspace, and assigns binarized values to 3D grid points to represent obstacles, allowing for efficient collision detection and path planning while avoiding obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth sensors are used to model the robot working environment in real-time, then measurement precision and adaptability are improved, but device complexity and computational burden increase
Solution Approach 1:
The patent segments the continuous 3D workspace into discrete volumetric grid cells, transforming the complex continuous space into a manageable discrete representation. Each grid cell can be independently processed and classified as occupied or free, significantly reducing computational complexity while maintaining environmental modeling accuracy.
Solution Approach 2:
The patent transforms the problem from continuous 3D coordinate space to a discrete 3D grid index space, adding the dimension of discretization. This allows the system to handle depth sensor data more efficiently by working with grid indices rather than continuous coordinates, reducing computational burden while preserving measurement precision.
2Measurement precision
If a large number of 3D points from depth sensors are used for path planning, then obstacle detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the workspace into a volumetric grid where each cell aggregates multiple 3D points. Instead of processing millions of individual depth points, the system processes a much smaller number of grid cells, dramatically reducing processing time while maintaining obstacle detection accuracy through the discrete representation.
Solution Approach 2:
The patent uses a fixed-resolution volumetric grid that may represent space with higher precision than strictly necessary in some regions, but this ensures consistent and reliable obstacle detection across the entire workspace. The discretization provides sufficient precision for safety-critical applications while enabling real-time processing.
3Device complexity
If simple geometric shapes are used to model obstacles, then device complexity is reduced, but measurement precision and adaptability to complex scenes deteriorate
Solution Approach 1:
The patent segments complex obstacles into multiple volumetric grid cells, allowing arbitrary and complex obstacle shapes to be represented without requiring complex geometric modeling. Each grid cell independently represents a portion of the obstacle, enabling accurate representation of complex scenes while keeping the underlying data structure simple and uniform.
Solution Approach 2:
The patent changes the representation parameter from continuous geometric descriptions to discrete grid occupancy values. This transformation allows complex obstacle shapes to be represented with simple binary or multi-valued grid cells, reducing modeling complexity while preserving measurement precision through the discrete spatial representation.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for robot path planning. Depth data of obstacles, acquired by depth sensors deployed in a 3D robot workspace and represented with respect to a sensor coordinate system, is transformed into depth data with respect to a robot coordinate system. The 3D robot workspace is discretized to generate 3D grid points representing a discretized 3D robot workspace. Based on the depth data with respect to the robot coordinate system, binarized values are assigned to at least some of 3D grid points to generate a binarized representation for the obstacles present in the 3D robot workspace. With respect to one or more sensing points associated with a part of a robot, it is determined whether the part is to collide with any obstacle. Based on the determining, a path is planned for the robot to move along while avoiding any obstacle.