Temporal Correlations and Persistence in the Kinetic Ising Model: the Role of Temperature

We study the statistical properties of the sum $S_t=\int_{0}^{t}dt' \sigma_{t'}$, that is the difference of time spent positive or negative by the spin $\sigma_{t}$, located at a given site of a $D$-dimensional Ising model evolving under Glauber dynamics from a random initial configuration. We investigate the distribution of $S_{t}$ and the first-passage statistics (persistence) of this quantity. We discuss successively the three regimes of high temperature ($T>T_{c}$), criticality ($T=T_c$), and low temperature ($T<T_{c}$). We discuss in particular the question of the temperature dependence of the persistence exponent $\theta$, as well as that of the spectrum of exponents $\theta(x)$, in the low temperature phase. The probability that the temporal mean $S_t/t$ was always larger than the equilibrium magnetization is found to decay as $t^{-\theta-\frac12}$. This yields a numerical determination of the persistence exponent $\theta$ in the whole low temperature phase, in two dimensions, and above the roughening transition, in the low-temperature phase of the three-dimensional Ising model.Comment: 21 pages, 11 PostScript figures included (1 color figure

Dynamic collision avoidance system for a manipulator based on RGB-D data

The new paradigms of Industry 4.0 demand the collabora- tion between robot and humans. They could help and collaborate each other without any additional safety unlike other manipulators. The robot should have the ability of acquire the environment and plan (or re-plan) on-the- y the movement avoiding the obstacles and people. This paper proposes a system that acquires the environment space, based on a kinect sensor, performs the path planning of a UR5 manipulator for pick and place tasks while avoiding the objects, based on the point cloud from kinect. Results allow to validate the proposed system.Project ”TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational. Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). This work is also financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio

Reactive control of autonomous drones

Aerial drones, ground robots, and aquatic rovers enable mobile applications that no other technology can realize with comparable flexibility and costs. In existing platforms, the low-level control enabling a drone's autonomous movement is currently realized in a time-triggered fashion, which simplifies implementations. In contrast, we conceive a notion of reactive control that supersedes the time-triggered approach by leveraging the characteristics of existing control logic and of the hardware it runs on. Using reactive control, control decisions are taken only upon recognizing the need to, based on observed changes in the navigation sensors. As a result, the rate of execution dynamically adapts to the circumstances. Compared to time-triggered control, this allows us to: i) attain more timely control decisions, ii) improve hardware utilization, iii) lessen the need to overprovision control rates. Based on 260+ hours of real-world experiments using three aerial drones, three different control logic, and three hardware platforms, we demonstrate, for example, up to 41% improvements in control accuracy and up to 22% improvements in flight time

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Design and Evaluation of Path Planning Decision Support for Planetary Surface Exploration

Human intent is an integral part of real-time path planning and re-planning, thus any decision aiding system must support human-automation interaction. The appropriate balance between humans and automation for this task has previously not been adequately studied. In order to better understand task allocation and collaboration between humans and automation for geospatial path problem solving, a prototype path planning aid was developed and tested. The focus was human planetary surface exploration, a high risk, time-critical domain, but the scenario is representative of any domain where humans path plan across uncertain terrain. Three visualizations, including elevation contour maps, a novel visualization called levels of equal costs, and a combination of the two were tested along with two levels of automation. When participants received the lower level of automation assistance, their path costs errors were less than 35% of the optimal, and they integrated manual sensitivity analysis strategies. When participants used the higher level of automation assistance, path costs errors were reduced to a few percentages, and they saved on average 1.5 minutes in the task. However, this increased performance came at the price of decreased situation awareness and automation bias.We would like to acknowledge the NASA Harriett G. Jenkins Predoctoral Fellowship and the Office of Naval Research for sponsoring this research

Multi-agent Poli-RRT* Optimal constrained RRT-based planning for multiple vehicles with feedback linearisable dynamics

Planning a trajectory that is optimal according to some performance criterion, collision-free, and feasible with respect to dynamic and actuation constraints is a key functionality of an autonomous vehicle. Poli-RRT* is a sample-based planning algorithm that serves this purpose for a single vehicle with feedback linearisable dynamics. This paper extends Poli-RRT* to a multi-agent cooperative setting where multiple vehicles share the same environment and need to avoid each other besides some static obstacles

A randomized attitude slew planning algorithm for autonomous spacecraft

The ability to autonomously generate and execute large angle attitude maneuvers, while operating under a number of celestial and dynamical constraints, is a key factor in the development of several future space platforms. In this paper we propose a ran-domized attitude slew planning algorithm for autonomous spacecraft, which is able to address a variety of pointing constraints, including bright object avoidance and ground link maintenance, as well as constraints on the control inputs and spacecraft states, and integral constraints such as those deriving from thermal control requirements. Moreover, through the scheduling of feedback control policies, the algorithm provides a consistent decoupling between low-level control and attitude motion planning, and is robust with respect to uncertainties in the spacecraft dynamics and environmental disturbances. Sim-ulation examples are presented and discussed

Intense or Spatially Heterogeneous Predation Can Select against Prey Dispersal

Dispersal theory generally predicts kin competition, inbreeding, and temporal variation in habitat quality should select for dispersal, whereas spatial variation in habitat quality should select against dispersal. The effect of predation on the evolution of dispersal is currently not well-known: because predation can be variable in both space and time, it is not clear whether or when predation will promote dispersal within prey. Moreover, the evolution of prey dispersal affects strongly the encounter rate of predator and prey individuals, which greatly determines the ecological dynamics, and in turn changes the selection pressures for prey dispersal, in an eco-evolutionary feedback loop. When taken all together the effect of predation on prey dispersal is rather difficult to predict. We analyze a spatially explicit, individual-based predator-prey model and its mathematical approximation to investigate the evolution of prey dispersal. Competition and predation depend on local, rather than landscape-scale densities, and the spatial pattern of predation corresponds well to that of predators using restricted home ranges (e.g. central-place foragers). Analyses show the balance between the level of competition and predation pressure an individual is expected to experience determines whether prey should disperse or stay close to their parents and siblings, and more predation selects for less prey dispersal. Predators with smaller home ranges also select for less prey dispersal; more prey dispersal is favoured if predators have large home ranges, are very mobile, and/or are evenly distributed across the landscape