37,651 research outputs found
From Monocular SLAM to Autonomous Drone Exploration
Micro aerial vehicles (MAVs) are strongly limited in their payload and power
capacity. In order to implement autonomous navigation, algorithms are therefore
desirable that use sensory equipment that is as small, low-weight, and
low-power consuming as possible. In this paper, we propose a method for
autonomous MAV navigation and exploration using a low-cost consumer-grade
quadrocopter equipped with a monocular camera. Our vision-based navigation
system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense
reconstruction of the environment in real-time. Since LSD-SLAM only determines
depth at high gradient pixels, texture-less areas are not directly observed so
that previous exploration methods that assume dense map information cannot
directly be applied. We propose an obstacle mapping and exploration approach
that takes the properties of our semi-dense monocular SLAM system into account.
In experiments, we demonstrate our vision-based autonomous navigation and
exploration system with a Parrot Bebop MAV
Directed Exploration using a Modified Distance Transform
Mobile robots operating in unknown environments need to build maps. To do so they must have an exploration algorithm to plan a path. This algorithm should guarantee that the whole of the environment, or at least some designated area, will be mapped. The path should also be optimal in some sense and not simply a "random walk" which is clearly inefficient. When multiple robots are involved, the algorithm also needs to take advantage of the fact that the robots can share the task. In this paper we discuss a modification to the well-known distance transform that satisfies these requirements
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
A neural circuit for navigation inspired by C. elegans Chemotaxis
We develop an artificial neural circuit for contour tracking and navigation
inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to
harness the computational advantages spiking neural networks promise over their
non-spiking counterparts, we develop a network comprising 7-spiking neurons
with non-plastic synapses which we show is extremely robust in tracking a range
of concentrations. Our worm uses information regarding local temporal gradients
in sodium chloride concentration to decide the instantaneous path for foraging,
exploration and tracking. A key neuron pair in the C. elegans chemotaxis
network is the ASEL & ASER neuron pair, which capture the gradient of
concentration sensed by the worm in their graded membrane potentials. The
primary sensory neurons for our network are a pair of artificial spiking
neurons that function as gradient detectors whose design is adapted from a
computational model of the ASE neuron pair in C. elegans. Simulations show that
our worm is able to detect the set-point with approximately four times higher
probability than the optimal memoryless Levy foraging model. We also show that
our spiking neural network is much more efficient and noise-resilient while
navigating and tracking a contour, as compared to an equivalent non-spiking
network. We demonstrate that our model is extremely robust to noise and with
slight modifications can be used for other practical applications such as
obstacle avoidance. Our network model could also be extended for use in
three-dimensional contour tracking or obstacle avoidance
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