Legged Robot Design Automation via Computational Optimization
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Solution Overview
Problem
Current robotic design processes for legged robots are manual, time-consuming, and require expertise, often relying on manual efforts and inspiration from nature, with limited automation and optimization of morphology and motion for specific tasks, leading to inefficient designs and high costs.
Innovation Solution
A system and method utilizing a three-stage optimization process: motion optimization, morphology optimization, and link length optimization, using software modules to automate the design of legged robots by optimizing motion trajectories, morphology, and link lengths for specific tasks, reducing manual work and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design efforts led by experienced engineers are used, then robot design quality and expertise are improved, but design time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical design processes with computational optimization algorithms. The system uses automated software to perform morphology optimization, link length optimization, and motion optimization, substituting human engineers' manual work with computer-based computational methods that can evaluate numerous design configurations rapidly without requiring extensive human time investment.
Solution Approach 2:
The design system performs self-optimization by automatically evaluating design configurations and selecting optimal parameters without continuous human intervention. The computational algorithms independently optimize morphology, link lengths, and motion trajectories based on specified task requirements, enabling the system to serve itself in the design process while maintaining high quality standards.
2Reliability
If comprehensive optimization of morphology and motion is performed, then robot performance for specific tasks is improved, but computational complexity and design process complexity increase
Solution Approach 1:
The patent divides the complex design optimization process into three distinct sequential stages: morphology optimization (determining overall structure), link length optimization (specific dimension tuning), and motion optimization (trajectory and control parameters). This segmentation allows each sub-problem to be solved independently with appropriate algorithms, reducing the overall computational complexity compared to attempting simultaneous optimization of all parameters.
Solution Approach 2:
The system performs preliminary optimization of morphology and link lengths before conducting motion optimization. By pre-determining the optimal structural configuration and dimensions in earlier stages, the subsequent motion optimization operates on a fixed, optimized platform, reducing the search space and computational burden of the final motion planning stage.
3Productivity
If automated optimization algorithms are used, then design efficiency and productivity are improved, but extent of automation increases requiring sophisticated software systems
Solution Approach 1:
The automated system is divided into three separate optimization modules that can be implemented and executed independently. Each module handles a specific aspect of the design (morphology, link lengths, motion), allowing for simpler, more manageable software implementations compared to a single monolithic automated system, while still achieving high overall productivity through their coordinated operation.
Data Source
AI summary
A robot design system, and associated method, that is particularly well-suited for legged robots (e.g., monopods, bipeds, and quadrupeds). The system implements three stages or modules: (a) a motion optimization module; (b) a morphology optimization module; and (c) a link length optimization module. The motion optimization module outputs motion trajectories of the robot's center of mass (COM) and force effectors. The morphology optimization module uses as input the optimized motion trajectories and a library of modular robot components and outputs an optimized robot morphology, e.g., a parameterized mechanical design in which the number of links in each of the legs and other parameters are optimized. The link length optimization module takes this as input and outputs optimal link lengths for a particular task such that the design of a robot is more efficient. The system solves the problem of automatically designing legged robots for given locomotion tasks by numerical optimization.