Optimizing Modular Robot Composition: A Lexicographic Genetic Algorithm Approach
arXiv (Cornell University)(2023)
摘要
Industrial robots are designed as general-purpose hardware with limited
ability to adapt to changing task requirements or environments. Modular robots,
on the other hand, offer flexibility and can be easily customized to suit
diverse needs. The morphology, i.e., the form and structure of a robot,
significantly impacts the primary performance metrics acquisition cost, cycle
time, and energy efficiency. However, identifying an optimal module composition
for a specific task remains an open problem, presenting a substantial hurdle in
developing task-tailored modular robots. Previous approaches either lack
adequate exploration of the design space or the possibility to adapt to complex
tasks. We propose combining a genetic algorithm with a lexicographic evaluation
of solution candidates to overcome this problem and navigate search spaces
exceeding those in prior work by magnitudes in the number of possible
compositions. We demonstrate that our approach outperforms a state-of-the-art
baseline and is able to synthesize modular robots for industrial tasks in
cluttered environments.
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关键词
modular robot composition,lexicographic genetic algorithm,genetic algorithm
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