Gradient-Based Robot Design Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for designing 3D robot body models lack efficiency in optimizing the distribution of deformable and rigid materials for optimal performance in motion metrics, leading to suboptimal robot designs.
Innovation Solution
A computer-implemented method that uses a gradient-based optimization approach to determine the optimal distribution of density values and actuation coefficients over voxels in a 3D space, simulating the robot's behavior and actuation to achieve improved motion performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gradient-based optimization is used to optimize material distribution and actuation layout, then robot motion performance is improved, but computational complexity increases
Solution Approach 1:
The design space is segmented into discrete voxels, allowing the optimization problem to be broken down into manageable units. Each voxel can independently represent material presence or absence, enabling gradient-based optimization to efficiently explore the design space while maintaining computational tractability through the discrete structure.
Solution Approach 2:
The patent transforms the continuous material distribution problem into a discrete optimization problem by introducing density values that can be optimized through gradient-based methods. The actuation coefficients are similarly parameterized, allowing the optimization algorithm to systematically adjust parameters and converge to optimal solutions for robot motion performance.
2Manufacturing precision
If detailed optimization of density distribution and actuation coefficients is performed, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces traditional iterative trial-and-error design methods with gradient-based optimization, which uses mathematical gradients to directly guide the search toward optimal solutions. This substitution of the optimization mechanism significantly reduces computational time while achieving detailed precision in material distribution and actuation coefficient determination.
Solution Approach 2:
The patent uses voxel-based discrete representations to model continuous material distributions, creating a simplified computational copy of the physical system. This discrete model allows for efficient optimization calculations while maintaining sufficient fidelity to guide the actual manufacturing process with high precision.
Data Source
AI summary
A computer-implemented method for designing a 3D robot body model representing a robot body formed in one or more materials. The method comprises obtaining an objective function based on predetermined parameters quantifying a motion metric of the robot. The predetermined parameters include a plurality of voxels forming a gridding of a 3D space, one or more parameters related to the one or more materials, and an actuation function which represents an actuation signal. The design variables include a distribution of density values over the plurality of voxels, and a distribution of actuation coefficients over the plurality voxels. The method further comprises exploring the design variables so as to perform a gradient-based optimization of the objective function, thereby obtaining an optimal continuous value of the design variables, and determining a 3D robot body model based on the optimal continuous value of the design variables.