Offline GA-Based Optimization for Heterogeneous Modular Multiconfigurable Chained Microrobots

Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

doi:10.1109/tmech.2012.2220560

Cited by 7 publications

(2 citation statements)

References 19 publications

(13 reference statements)

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“…To reduce the search space, Farritor et al (49) presented a hierarchical synthesis approach, which grouped useful combinations of basic modules; however, no dynamic task requirements were considered. Composition synthesis approaches based on genetic algorithms were presented in (49)(50)(51)(52)(53), also without dynamic task requirements.…”

Section: Ensuring Optimalitymentioning

confidence: 99%

Effortless creation of safe robots from modules through self-programming and self-verification

et al. 2019

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Industrial robots cannot be reconfigured to optimally fulfill a given task and often have to be caged to guarantee human safety. Consequently, production processes are meticulously planned so that they last for long periods to make automation affordable. However, the ongoing trend toward mass customization and small-scale manufacturing requires purchasing new robots on a regular basis to cope with frequently changing production. Modular robots are a natural answer: Robots composed of standardized modules can be easily reassembled for new tasks, can be quickly repaired by exchanging broken modules, and are cost-effective by mass-producing standard modules usable for a large variety of robot types. Despite these advantages, modular robots have not yet left research laboratories because an expert must reprogram each new robot after assembly, rendering reassembly impractical. This work presents our set of interconnectable modules (IMPROV), which programs and verifies the safety of assembled robots themselves. Experiments show that IMPROV robots retained the same control performance as nonmodular robots, despite their reconfigurability. With respect to human-robot coexistence, our user study shows a reduction of robot idle time by 36% without compromising on safety using our self-verification concept compared with current safety standards. We believe that by using self-programming and self-verification, modular robots can transform current automation practices.

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Section: Ensuring Optimalitymentioning

confidence: 99%

Effortless creation of safe robots from modules through self-programming and self-verification

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Evolutionary algorithms have also been used to optimize reactive locomotion controllers based on HyperNEAT [57], chemical hormones models [58] and fractal genetic regulatory network [59]. For co-evolution of morphology and control, Brunete et al [60] represented the morphology of a heterogeneous snake-like microrobot directly in the chromosome, Faíña et al [61] represented a legged robot morphology as a tree, and Bongard and Pfeifer [62] evolved a genetic regulatory that would direct the growth of the robot instead of a direct representation.…”

Section: Adaptive Self-reconfigurable Modular Robotsmentioning

confidence: 99%