Hybrid control of a robot — a case study

Ravn, Anders P.; Rischel, Hans; Conrad, Finn; Andersen, Torben Ole

doi:10.1007/3-540-60472-3_20

“…For example, an escape circuit might suppress and override a swimming circuit [69]. Functionally decomposing behavior, and assigning dedicated control to each function, has historically been the traditional approach to robotic control, such as in traditional finite state machines [102,112,125,131]. The drawback is that controlling a wide repertoire of behaviors can lead to a combinatorial explosion, making this approach impractical in general, and it is clearly not used for most animal behaviors.…”

Section: Introductionmentioning

confidence: 99%

Control for multifunctionality: bioinspired control based on feeding in Aplysia californica

Webster‐Wood

¹

,

Gill

²

,

Thomas

³

et al. 2020

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Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bioinspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework Victoria A. Webster-Wood,

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“…For example, an escape circuit might suppress and override a swimming circuit [61]. Functionally decomposing behavior, and assigning dedicated control to each function, has historically been the traditional approach to robotic control, such as in traditional finite state machines [93,122,103,116]. The draw-back is that controlling a wide repertoire of behaviors can lead to a combinatorial explosion, making this approach impractical in general, and it is clearly not used for most animal behaviors.…”

Section: Introductionmentioning

confidence: 99%

Control for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica

Webster-Wood,

Gill,

Thomas

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bio-inspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural

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“…Typically they are control applications and are often used in (safety/time) critical systems. Examples include air and sea traffic management systems [Godhavn et al · 1996], automated highway systems [Varaiya 1993], industrial automation [Ravn et al 1995;Varaiya 1993], and automotive control systems [Antsaklis et al 1995;Antsaklis 1997;Antsaklis et al 1999;Stauner et al 1997;Girard et al 2005].…”

Section: Introductionmentioning

confidence: 99%