Information Surfing for Radiation Building

We develop a control scheme for a group of mobile sensors to map radiation over a given planar polygonal region. The advantage of this methodology is that it provides quick situational awareness regarding radiation levels, which is being updated and refined in real- time as more measurements become available. The control algorithm is based on the concept of information surfing, where navigation is done by following information gradients, taking into account sensing performance and the dynamics of the observed proces

Information Surfing for Model-driven Radiation Mapping

In this report we develop a control scheme to coordinate a group of mobile sensors for radiation mapping of a given planar polygon region. The control algorithm is based on the concept of information surfing, where navigation is done by means of following information gradients, taking into account sensing performance as well as inter-robot communication range limitations. The control scheme provably steers mobile sensors to locations at which they maximize the information content of their measurement data, and the asymptotic properties of our information metric with respect to time ensures that no local information metric extremum traps the sensors indefinitely. In addition, the inherent synergy of the mobile sensor group facilitates the temporal erosion of such extremum configurations. Information surfing allows for reactive mobile sensor network behavior and adaptation to environmental changes, as well as human retasking

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

Optimal decision making for sperm chemotaxis in the presence of noise

For navigation, microscopic agents such as biological cells rely on noisy sensory input. In cells performing chemotaxis, such noise arises from the stochastic binding of signaling molecules at low concentrations. Using chemotaxis of sperm cells as application example, we address the classic problem of chemotaxis towards a single target. We reveal a fundamental relationship between the speed of chemotactic steering and the strength of directional fluctuations that result from the amplification of noise in the chemical input signal. This relation implies a trade-off between slow, but reliable, and fast, but less reliable, steering. By formulating the problem of optimal navigation in the presence of noise as a Markov decision process, we show that dynamic switching between reliable and fast steering substantially increases the probability to find a target, such as the egg. Intriguingly, this decision making would provide no benefit in the absence of noise. Instead, decision making is most beneficial, if chemical signals are above detection threshold, yet signal-to-noise ratios of gradient measurements are low. This situation generically arises at intermediate distances from a target, where signaling molecules emitted by the target are diluted, thus defining a `noise zone' that cells have to cross. Our work addresses the intermediate case between well-studied perfect chemotaxis at high signal-to-noise ratios close to a target, and random search strategies in the absence of navigation cues, e.g. far away from a target. Our specific results provide a rational for the surprising observation of decision making in recent experiments on sea urchin sperm chemotaxis. The general theory demonstrates how decision making enables chemotactic agents to cope with high levels of noise in gradient measurements by dynamically adjusting the persistence length of a biased persistent random walk.Comment: 9 pages, 5 figure

Concepts of GPCR-controlled navigation in the immune system

G-protein-coupled receptor (GPCR) signaling is essential for the spatiotemporal control of leukocyte dynamics during immune responses. For efficient navigation through mammalian tissues, most leukocyte types express more than one GPCR on their surface and sense a wide range of chemokines and chemoattractants, leading to basic forms of leukocyte movement (chemokinesis, haptokinesis, chemotaxis, haptotaxis, and chemorepulsion). How leukocytes integrate multiple GPCR signals and make directional decisions in lymphoid and inflamed tissues is still subject of intense research. Many of our concepts on GPCR-controlled leukocyte navigation in the presence of multiple GPCR signals derive from in vitro chemotaxis studies and lower vertebrates. In this review, we refer to these concepts and critically contemplate their relevance for the directional movement of several leukocyte subsets (neutrophils, T cells, and dendritic cells) in the complexity of mouse tissues. We discuss how leukocyte navigation can be regulated at the level of only a single GPCR (surface expression, competitive antagonism, oligomerization, homologous desensitization, and receptor internalization) or multiple GPCRs (synergy, hierarchical and non-hierarchical competition, sequential signaling, heterologous desensitization, and agonist scavenging). In particular, we will highlight recent advances in understanding GPCR-controlled leukocyte navigation by intravital microscopy of immune cells in mice

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

Method and apparatus for predicting the direction of movement in machine vision

A computer-simulated cortical network is presented. The network is capable of computing the visibility of shifts in the direction of movement. Additionally, the network can compute the following: (1) the magnitude of the position difference between the test and background patterns; (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern; and (3) the direction of a test pattern moved relative to a textured background. The direction of movement of an object in the field of view of a robotic vision system is detected in accordance with nonlinear Gabor function algorithms. The movement of objects relative to their background is used to infer the 3-dimensional structure and motion of object surfaces