A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

A Hybrid Strong/Weak Coupling Approach to Jet Quenching

We propose and explore a new hybrid approach to jet quenching in a strongly coupled medium. The basis of this phenomenological approach is to treat physics processes at different energy scales differently. The high-$Q^2$ processes associated with the QCD evolution of the jet from production as a single hard parton through its fragmentation, up to but not including hadronization, are treated perturbatively. The interactions between the partons in the shower and the deconfined matter within which they find themselves lead to energy loss. The momentum scales associated with the medium (of the order of the temperature) and with typical interactions between partons in the shower and the medium are sufficiently soft that strongly coupled physics plays an important role in energy loss. We model these interactions using qualitative insights from holographic calculations of the energy loss of energetic light quarks and gluons in a strongly coupled plasma, obtained via gauge/gravity duality. We embed this hybrid model into a hydrodynamic description of the spacetime evolution of the hot QCD matter produced in heavy ion collisions and confront its predictions with jet data from the LHC. The holographic expression for the energy loss of a light quark or gluon that we incorporate in our hybrid model is parametrized by a stopping distance. We find very good agreement with all the data as long as we choose a stopping distance that is comparable to but somewhat longer than that in ${\cal N}=4$ supersymmetric Yang-Mills theory. For comparison, we also construct alternative models in which energy loss occurs as it would if the plasma were weakly coupled. We close with suggestions of observables that could provide more incisive evidence for, or against, the importance of strongly coupled physics in jet quenching.Comment: 47 pages, 10 figures, typos corrected. Small mistake in the implementation of the Gluaber Monte-Carlo for the collision geometry corrected. Correction results in small changes to values of fitted parameters and to plots. No changes to any discussion or conclusion

The New Age of Hybridity and Clash of Norms: China, BRICS and Challenges of Global Governance in a Post-liberal International Order

This article sketches an analytical framework to account for new patterns of global governance. We characterize the emergent post-liberal international order as a new age of hybridity, which signifies that no overriding set of paradigms dominate global governance. Instead we have a complex web of competing norms, which creates new opportunities as well as major elements of instability, uncertainty and anxiety. In the age of hybridity, non-Western great powers (led by China) play an increasingly counter-hegemonic role in shaping new style multilateralism – ontologically fragmented, normatively inconsistent, and institutionally incoherent. We argue that democracy paradox constitutes the fundamental issue at stake in this new age of hybridity. On the one hand, global power transitions seem to enable ‘democratization of globalization’ by opening more space to the hitherto excluded non-Western states to make their voices heard. On the other hand, emerging pluralism in global governance is accompanied by the regression of liberal democracy and spread of illiberalism that enfeeble ‘globalization of democratization.

Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process

Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.Comment: The paper has been accepted to the International Conference on Robotics and Automation (ICRA2018