Computational Co-Design for Variable Geometry Truss
Jianzhe Gu, Lining Yao

TL;DR
This paper presents a learning-based co-design approach combining genetic algorithms and reinforcement learning to optimize control and design of variable geometry truss robots, enabling complex motions with limited controls.
Contribution
It introduces a novel co-design framework for VGTs that integrates GA and RL, tailored to the PneuMesh system, to improve control and shape adaptability.
Findings
Enables a VGT robotic table to perform various motions.
Optimizes channel grouping and control using GA and RL.
Supports adaptive, inclusive design for different use cases.
Abstract
Living creatures and machines interact with the world through their morphology and motions. Recent advances in creating bio-inspired morphing robots and machines have led to the study of variable geometry truss (VGT), structures that can approximate arbitrary geometries and has large degree of freedom to deform. However, they are limited to simple geometries and motions due to the excessively complex control system. While a recent work PneuMesh solves this challenge with a novel VGT design that introduces a selective channel connection strategy, it imposes new challenge in identifying effective channel groupings and control methods. Building on top of the hardware concept presented in PneuMesh, we frame the challenge into a co-design problem and introduce a learning-based model to find a sub-optimal design. Specifically, given an initial truss structure provided by a human designer,…
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Taxonomy
TopicsAdvanced Materials and Mechanics · Modular Robots and Swarm Intelligence · Cellular Mechanics and Interactions
MethodsGenetic Algorithms
