6 research outputs found
Flying over the reality gap: From simulated to real indoor airships
Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approac
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
It is true that the "best" neural network is not necessarily the one with the
most "brain-like" behavior. Understanding biological intelligence, however, is
a fundamental goal for several distinct disciplines. Translating our
understanding of intelligence to machines is a fundamental problem in robotics.
Propelled by new advancements in Neuroscience, we developed a spiking neural
network (SNN) that draws from mounting experimental evidence that a number of
individual neurons is associated with spatial navigation. By following the
brain's structure, our model assumes no initial all-to-all connectivity, which
could inhibit its translation to a neuromorphic hardware, and learns an
uncharted territory by mapping its identified components into a limited number
of neural representations, through spike-timing dependent plasticity (STDP). In
our ongoing effort to employ a bioinspired SNN-controlled robot to real-world
spatial mapping applications, we demonstrate here how an SNN may robustly
control an autonomous robot in mapping and exploring an unknown environment,
while compensating for its own intrinsic hardware imperfections, such as
partial or total loss of visual input.Comment: 8 pages, 3 Figures, International Conference on Neuromorphic Systems
(ICONS 2018
Flying over the Reality Gap: From Simulated to Real Indoor Airships
Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approach
A model of grid cells based on a twisted torus topology
The grid cells of the rat medial entorhinal cortex (MEC) show an increased firing frequency when the position of the animal correlates with multiple regions of the environment that are arranged in regular triangular grids. Here, we describe an artificial neural network based on a twisted torus topology, which allows for the generation of regular triangular grids. The association of the activity of pre-defined hip-pocampal place cells with entorhinal grid cells allows for a highly robust-to-noise calibration mechanism, suggesting a role for the hippocampal back-projections to the entorhinal cortex