NeuroCopter: Neuromorphic Computation of 6D Ego-Motion of a Quadcopter

Landgraf, Tim; Wild, Benjamin; Ludwig, Tobias; Nowak, Philipp; Helgadottir, Lovisa Irpa; Daumenlang, Benjamin; Breinlinger, Philipp; Nawrot, Martin Paul; Rojas, Raúl

doi:10.1007/978-3-642-39802-5_13

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, these deep brain models have been augmented with biologically plausible perception systems capable of generating a compass signal from polarised light accurate to less than one degree in ideal conditions [104] through a matched-filter-like process [105]. Robotic implementations of these models have been instantiated and provide the necessary real-world verification of hypotheses and inspiration for engineers [106][107][108][109].…”

Section: Mental Faculties: Revealing the Neural Basis Of Invertebrate...mentioning

confidence: 99%

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

et al. 2023

View full text Add to dashboard Cite

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

show abstract

Section: Mental Faculties: Revealing the Neural Basis Of Invertebrate...mentioning

confidence: 99%

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Closing the loop from active sensing to associative memory formation and behavioral control requires to synchronize a (spiking) neural network at the adaptive layer with the sensory (reactive layer) and locomotor modules (basic layer). This will enable the simulation of virtual larva experiencing spatial and temporal dynamics in a virtual environment or on a robotic platform (87). Such model approaches will allow to test model hypotheses on sensorymotor integration and to infer predictions for experimental interventions such as optogenetic stimulation (45) or genetic manipulations (88)(89)(90)(91).…”

Section: Behavioral Intermittencymentioning

confidence: 99%

A realistic locomotory model of Drosophila larva for behavioral simulations

Sakagiannis

Jürgensen

Nawrot

2021

Preprint

View full text Add to dashboard Cite

The Drosophila larva is extensively used as model species in experiments where behavior is recorded via tracking equipment and evaluated via population-level metrics. Although larva locomotion neuromechanics have been studied in detail, no comprehensive model has been proposed for realistic simulations of foraging experiments directly comparable to tracked recordings. Here we present a virtual larva for simulating autonomous behavior, fitting empirical observations of spatial and temporal kinematics. We propose a trilayer behavior-based control architecture for larva foraging, allowing to accommodate increasingly complex behaviors. At the basic level, forward crawling and lateral bending are generated via coupled, interfering oscillatory processes under the control of an intermittency module, alternating between crawling bouts and pauses. Next, navigation in olfactory environments is achieved via active sensing and topdown modulation of bending dynamics by concentration changes. Finally, adaptation at the highest level entails associative learning. We could accurately reproduce behavioral experiments on autonomous free exploration, chemotaxis, and odor preference testing. Interindividual variability is preserved across virtual larva populations allowing for single animal and population studies. Our model is ideally suited to interface with neural circuit models of sensation, memory formation and retrieval, and spatial navigation.

show abstract

NeuroCopter: Neuromorphic Computation of 6D Ego-Motion of a Quadcopter

Cited by 2 publications

References 12 publications

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

A realistic locomotory model of Drosophila larva for behavioral simulations

Contact Info

Product

Resources

About