Coordinated motion of UGVs and a UAV

Coordination of autonomous mobile robots has received significant attention during the last two decades. Coordinated motion of heterogenous robot groups are more appealing due to the fact that unique advantages of different robots might be combined to increase the overall efficiency of the system. In this paper, a heterogeneous robot group composed of multiple Unmanned Ground Vehicles (UGVs) and an Unmanned Aerial Vehicle (UAV) collaborate in order to accomplish a predefined goal. UGVs follow a virtual leader which is defined as the projection of UAV’s position onto the horizontal plane. The UAV broadcasts its position at certain frequency. The position of the virtual leader and distances from the two closest neighbors are used to create linear and angular velocity references for each UGV. Several coordinated tasks have been presented and the results are verified by simulations where certain amount of communication delay between the vehicles is also considered. Results are quite promising

Comparing radio-tracking and visual detection methods to quantify group size measures

1. Average values of animal group sizes are prone to be overestimated in traditional field studies because small groups and singletons are easier to overlook than large ones. This kind of bias also applies for the method of locating groups by tracking previously radio-collared individuals in the wild. If the researcher randomly chooses a collared animal to locate a group to visit, a large group has higher probability to be selected than a small one, simply because it has more members.2. The question arises whether location of groups by means of finding collared animals has smaller or greater bias than searching for groups by visual observation. If the bias is smaller or same, this method can be recommended  for finding groups. However, such a comparison cannot be made by speculation, only by empirical investigation.3. The present study compares the two methods empirically, by statistically comparing group size measures (mean, median, quantiles, frequency distribution, and ‘typical group size’) between two data sets. These data sets  comprise of Rocky Mountain mule deer group size values collected in the same area during the same period of time, referring either to groups located by the traditional ‘search and observe method’ or located by tracking formerly collared individuals.4. All group size measures are statistically similar in the two samples, thus we conclude that the two methods yielded similar biases. Although the true group size measures are not known, we presume that both methods have overestimated them. We propose that these results do not necessary apply to other species, thus cannot be generalized. The reason for this is that bias may depend on factors specific to the species: bias of visual observation may depend on how well the species conceals itself in the existing habitat, and the bias associated with finding groups using collared animals is likely dependent on group size distribution and also on the proportion of collared animals in the population

Phase transitions during fruiting body formation in Myxococcus xanthus

The formation of a collectively moving group benefits individuals within a population in a variety of ways such as ultra-sensitivity to perturbation, collective modes of feeding, and protection from environmental stress. While some collective groups use a single organizing principle, others can dynamically shift the behavior of the group by modifying the interaction rules at the individual level. The surface-dwelling bacterium Myxococcus xanthus forms dynamic collective groups both to feed on prey and to aggregate during times of starvation. The latter behavior, termed fruiting-body formation, involves a complex, coordinated series of density changes that ultimately lead to three-dimensional aggregates comprising hundreds of thousands of cells and spores. This multi-step developmental process most likely involves several different single-celled behaviors as the population condenses from a loose, two-dimensional sheet to a three-dimensional mound. Here, we use high-resolution microscopy and computer vision software to spatiotemporally track the motion of thousands of individuals during the initial stages of fruiting body formation. We find that a combination of cell-contact-mediated alignment and internal timing mechanisms drive a phase transition from exploratory flocking, in which cell groups move rapidly and coherently over long distances, to a reversal-mediated localization into streams, which act as slow-spreading, quasi-one-dimensional nematic fluids. These observations lead us to an active liquid crystal description of the myxobacterial development cycle.Comment: 16 pages, 5 figure

Identifying the information for the visual perception of relative phase

The production and perception of coordinated rhythmic movement are very specifically structured. For production and perception, 0° mean relative phase is stable, 180° is less stable, and no other state is stable without training. It has been hypothesized that perceptual stability characteristics underpin the movement stability characteristics, which has led to the development of a phase-driven oscillator model (e.g., Bingham, 2004a, 2004b). In the present study, a novel perturbation method was used to explore the identity of the perceptual information being used in rhythmic movement tasks. In the three conditions, relative position, relative speed, and frequency (variables motivated by the model) were selectively perturbed. Ten participants performed a judgment task to identify 0° or 180° under these perturbation conditions, and 8 participants who had been trained to visually discriminate 90° performed the task with perturbed 90° displays. Discrimination of 0° and 180° was unperturbed in 7 out of the 10 participants, but discrimination of 90° was completely disrupted by the position perturbation and was made noisy by the frequency perturbation. We concluded that (1) the information used by most observers to perceive relative phase at 0° and 180° was relative direction and (2) becoming an expert perceiver of 90° entails learning a new variable composed of position and speed