Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research. WiMUST uses a team of cooperative autonomous ma- rine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicat- ing nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely au- tomatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring inter- vehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.Peer reviewe

B-type supergiants in the SMC: Rotational velocities and implications for evolutionary models

High-resolution spectra for 24 SMC and Galactic B-type supergiants have been analysed to estimate the contributions of both macroturbulence and rotation to the broadening of their metal lines. Two different methodologies are considered, viz. goodness-of-fit comparisons between observed and theoretical line profiles and identifying zeros in the Fourier transforms of the observed profiles. The advantages and limitations of the two methods are briefly discussed with the latter techniques being adopted for estimated projected rotational velocities (\vsini) but the former being used to estimate macroturbulent velocities. Only one SMC supergiant, SK 191, shows a significant degree of rotational broadening (\vsini $\simeq$ 90 \kms). For the remaining targets, the distribution of projected rotational velocities are similar in both our Galactic and SMC samples with larger values being found at earlier spectral types. There is marginal evidence for the projected rotational velocities in the SMC being higher than those in the Galactic targets but any differences are only of the order of 5-10 \kms, whilst evolutionary models predict differences in this effective temperature range of typically 20 to 70 \kms. The combined sample is consistent with a linear variation of projected rotational velocity with effective temperature, which would imply rotational velocities for supergiants of 70 \kms at an effective temperature of 28 000 K (approximately B0 spectral type) decreasing to 32 \kms at 12 000 K (B8 spectral type). For all targets, the macroturbulent broadening would appear to be consistent with a Gaussian distribution (although other distributions cannot be discounted) with an $\frac{1}{e}$ half-width varying from approximately 20 \kms at B8 to 60 \kms at B0 spectral types.Comment: 4 figures, 8 pages, submitted to Astronomy and Astrophysic

Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning

We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments, behavioral descriptors, and fitness. We specify different benchmarks based on the complexity of both the task and the agent controlled by a deep neural network. The benchmark uses standard Quality-Diversity metrics, including coverage, QD-score, maximum fitness, and an archive profile metric to quantify the relation between coverage and fitness. We also present how to quantify the robustness of the solutions with respect to environmental stochasticity by introducing corrected versions of the same metrics. We believe that our benchmark is a valuable tool for the community to compare and improve their findings. The source code is available online: https://github.com/adaptive-intelligent-robotics/QDaxComment: Accepted at GECCO Workshop on Quality Diversity Algorithm Benchmark

Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

Active inference is an ambitious theory that treats perception, inference, and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g., different environments or agent morphologies. In the literature, paradigms that share this independence have been summarized under the notion of intrinsic motivations. In general and in contrast to active inference, these models of motivation come without a commitment to particular inference and action selection mechanisms. In this article, we study if the inference and action selection machinery of active inference can also be used by alternatives to the originally included intrinsic motivation. The perception-action loop explicitly relates inference and action selection to the environment and agent memory, and is consequently used as foundation for our analysis. We reconstruct the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original features intact. Furthermore, we illustrate the connection to universal reinforcement learning by means of our formalism. Active inference research may profit from comparisons of the dynamics induced by alternative intrinsic motivations. Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that also share the biological plausibility of active inference

Death, dying and informatics: misrepresenting religion on MedLine

BACKGROUND: The globalization of medical science carries for doctors worldwide a correlative duty to deepen their understanding of patients' cultural contexts and religious backgrounds, in order to satisfy each as a unique individual. To become better informed, practitioners may turn to MedLine, but it is unclear whether the information found there is an accurate representation of culture and religion. To test MedLine's representation of this field, we chose the topic of death and dying in the three major monotheistic religions. METHODS: We searched MedLine using PubMed in order to retrieve and thematically analyze full-length scholarly journal papers or case reports dealing with religious traditions and end-of-life care. Our search consisted of a string of words that included the most common denominations of the three religions, the standard heading terms used by the National Reference Center for Bioethics Literature (NRCBL), and the Medical Subject Headings (MeSH) used by the National Library of Medicine. Eligible articles were limited to English-language papers with an abstract. RESULTS: We found that while a bibliographic search in MedLine on this topic produced instant results and some valuable literature, the aggregate reflected a selection bias. American writers were over-represented given the global prevalence of these religious traditions. Denominationally affiliated authors predominated in representing the Christian traditions. The Islamic tradition was under-represented. CONCLUSION: MedLine's capability to identify the most current, reliable and accurate information about purely scientific topics should not be assumed to be the same case when considering the interface of religion, culture and end-of-life care

The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control

The proposed architecture applies the principle of predictive coding and deep learning in a brain-inspired approach to robotic sensorimotor control. It is composed of many layers each of which is a recurrent network. The component networks can be spontaneously active due to the homeokinetic learning rule, a principle that has been studied previously for the purpose of self-organized generation of behavior. We present robotic simulations that illustrate the function of the network and show evidence that deeper networks enable more complex exploratory behavior