A Collaborative Visual Localization Scheme for a Low-Cost Heterogeneous Robotic Team with Non-Overlapping Perspectives

This paper presents and evaluates a relative localization scheme for a heterogeneous team of low-cost mobile robots. An error-state, complementary Kalman Filter was developed to fuse analytically-derived uncertainty of stereoscopic pose measurements of an aerial robot, made by a ground robot, with the inertial/visual proprioceptive measurements of both robots. Results show that the sources of error, image quantization, asynchronous sensors, and a non-stationary bias, were sufficiently modeled to estimate the pose of the aerial robot. In both simulation and experiments, we demonstrate the proposed methodology with a heterogeneous robot team, consisting of a UAV and a UGV tasked with collaboratively localizing themselves while avoiding obstacles in an unknown environment. The team is able to identify a goal location and obstacles in the environment and plan a path for the UGV to the goal location. The results demonstrate localization accuracies of 2cm to 4cm, on average, while the robots operate at a distance from each-other between 1m and 4m

Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People

This paper presents a near real-time, multi-stage classifier which identifies people and handguns in images, and then further assesses the threat-level that a person poses based on their body posture. The first stage consists of a convolutional neural network (CNN) that determines whether a person and a handgun are present in an image. If so, a second stage CNN is then used to estimate the pose of the person detected to have a handgun. Lastly, a feed-forward neural network (NN) makes the final threat assessment based on the joint positions of the person’s skeletal pose estimate from the previous stage. On average, this entire pipeline requires less than 1 second of processing time on a desktop computer. The model was trained using approximately 2,000 images and achieved a pistol and person detection rate of 22% and 55%, respectively. The final stage NN correctly identified the severity of the threat with 84% accuracy. The images used to train each stage of our multi-classifier model are available online. With an expanded dataset the accuracy of detecting people and pistols can likely be improved in the future

Myeloid neoplasm with histiocytosis and spleen tyrosine kinase fusion responds to fostamatinib

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A direct localization of a fast radio burst and its host

Fast radio bursts are astronomical radio flashes of unknown physical nature with durations of milliseconds. Their dispersive arrival times suggest an extragalactic origin and imply radio luminosities orders of magnitude larger than any other kind of known short-duration radio transient. Thus far, all FRBs have been detected with large single-dish telescopes with arcminute localizations, and attempts to identify their counterparts (source or host galaxy) have relied on contemporaneous variability of field sources or the presence of peculiar field stars or galaxies. These attempts have not resulted in an unambiguous association with a host or multi-wavelength counterpart. Here we report the sub-arcsecond localization of FRB 121102, the only known repeating burst source, using high-time-resolution radio interferometric observations that directly image the bursts themselves. Our precise localization reveals that FRB 121102 originates within 100 mas of a faint 180 uJy persistent radio source with a continuum spectrum that is consistent with non-thermal emission, and a faint (25th magnitude) optical counterpart. The flux density of the persistent radio source varies by tens of percent on day timescales, and very long baseline radio interferometry yields an angular size less than 1.7 mas. Our observations are inconsistent with the fast radio burst having a Galactic origin or its source being located within a prominent star-forming galaxy. Instead, the source appears to be co-located with a low-luminosity active galactic nucleus or a previously unknown type of extragalactic source. [Truncated] If other fast radio bursts have similarly faint radio and optical counterparts, our findings imply that direct sub-arcsecond localizations of FRBs may be the only way to provide reliable associations.Comment: Nature, published online on 4 Jan 2017, DOI: 10.1038/nature2079

Towards a Heterogeneous Swarm for Object Classification

Object classification capabilities and associated reactive swarm behaviors are implemented in a decentralized swarm of autonomous, heterogeneous unmanned aerial vehicles (UAVs). Each UAV possesses a separate capability to recognize and classify objects using the You Only Look Once (YOLO) neural network model. The UAVs communicate and share data through a swarm software architecture using an adhoc wireless network. When one UAV recognizes a particular object of interest, the entire swarm reacts with a pre-programmed behavior. Classification results of people and backpacks using our modified UAV detection platforms are provided, as well as a simulated demonstration of the reactive swarm behaviors with actual hardware and swarm software in the loop

What is a Robot Swarm: A Definition for Swarming Robotics

The swarm, a type of multi-agent system, has enjoyed a recent surge in popularity within the autonomous robotics field. Despite a variety of theoretical and simulated research work in the area of swarm theory and multi-agent artificial intelligence, the practical use of swarms remains limited. Though many limiting factors lie on the technical front, one limiting factor may be a lack of appreciation for swarm capabilities and applications as opposed to those of conventional robotics. To help address the latter limiting factor, this paper proposes a definition of a swarm in the context of autonomous robotics, describes many real-world problems that can be addressed through use of swarms, and details current applications of swarming robotic systems

Artificial Intelligence Ethics: Governance through Social Media

A proposal is presented to facilitate machine self-learning of ethical behavior via human-curated training using online human behavioral data such as that found on social media and related sites. The proposed training data set is a mixture of human behavioral data found on social media and related sites that exhibit a wide variety of both ethical and unethical behavior which can help an artificially intelligent machine make ethical decisions during the process of solving real-world problems. The rapid proliferation of artificial intelligence (AI) applications worldwide highlights the need for normativity to protect individual rights, such as privacy, and the promotion of the common good; in other words, ethics. Governance of such widespread applications of AI as speech recognition, facial recognition, tracking of individuals using their personal electronic devices, etc., is needed to prevent abuses of such technologies by corporations or national governments. This paper presents a systemic view of the complexity of using principle-based governance to promote the ethical use of AI without unnecessarily hindering technological innovations needed to advance the state of the art in AI technology

Towards a General-Purpose, Replicable, Swarm-Capable Unmanned Aircraft System

This paper describes an effort to create a general-purpose Unmanned Aircraft System (UAS) swarm using entirely Commercial Off-the-Shelf (COTS) parts and a reusable swarming software architecture. The software architecture used in this research was originally designed for a UAS warfare competition in 2017 called the Service Academies Swarm Challenge (SASC), hosted by the Defense Advanced Research Projects Agency (DARPA). The SASC software is a multipurpose swarm-control software architecture that allows a swarm to be tailored to many different purposes by third-parties. However, the UASs used in the original SASC competition contain custom parts which have begun to deteriorate over years of use. A COTS UAS solution using the SASC swarm architecture is the next step towards expanding the usefulness of the swarm so that it can be deployed, replicated, modified, and generalized to suit many different needs in a variety of sectors to include homeland security and defense