Podcasts about autonomous navigation

Ottonomy's Founder and CEO, Ritukar Vijay, joins Coruzant Technologies for the Digital Executive podcast. He shares his passion for working with autonomous vehicles, navigation, robotic process automation (RPA), and AI since college.  His dream and hard work has paid off with the launch of this successful product Ottonomy.

The DoD and the Trusted Capital Marketplace have identified autonomous systems as one of the 27 areas in which commercial technology could be leveraged for military applications. In my discussion with Pete DeNagy, a subject matter expert on both autonomous systems and 5G, we tackle this challenge head on. After I define the somewhat unique characteristics of the military use case for autonomous systems (and the unique complexities), Pete explains how the commercial world is implementing autonomous vehicles . . . and be prepared; it's likely different than you think . . . at least it was to me. Rather than develop navigation systems that can inherently mimic the cognitive and control capabilities of a human driver, automotive manufacturers (and government agencies) are implementing communications-centric autonomous navigation systems, which leverage 5G, low latency, high bandwidth, and other properties as substitutes for the compute intensive (and algorithmically complex) alternative of mimicking human drivers. Will this work in the battlefield? That's where Pete illustrates how it can . . . what the key drivers are, what the central innovations that are required, and the rather short timeline that is needed to achieve this. Emails: mark@commercebasix.com and pdenagy@acommence.com1

OSIRIS REx tested a new autonomous navigation system designed to protect the spacecraft during its descent to the rock-strewn surface of asteroid Bennu.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.30.070755v1?rss=1 Authors: Drenkow, N., Joyce, J., Matelsky, J., Larabi, R., Heiko, J., Kleissas, D., Wester, B., Johnson, E. C., Gray-Roncal, W. R. Abstract: As biological imaging datasets continue to grow in size, extracting information from large image volumes presents a computationally intensive challenge. State-of-the-art algorithms are almost entirely dominated by the use of convolutional neural network approaches that may be difficult to run at scale given schedule, cost, and resource limitations. We demonstrate a novel solution for high-resolution electron microscopy brain image volumes that permits the identification of individual neurons and synapses. Instead of conventional approaches whereby voxels are labelled according to the neuron or neuron segment to which they belong, we instead focus on extracting the underlying brain graph represented by synaptic connections between individual neurons while also identifying key features like skeleton similarity and path length. This graph represents a critical step and scaffold for understanding the structure of neuronal circuitry. Our approach recasts the segmentation problem to one of path-finding between keypoints (i.e., connectivity) in an information sharing framework using virtual agents. We create a family of sensors which follow local decision-making rules that perform computationally cheap operations on potential fields to perform tasks such as avoiding cell membranes and finding synapses. These enable a swarm of virtual agents to efficiently and robustly traverse three-dimensional datasets, create a sparse segmentation of pathways, and capture connectivity information. We achieve results that meet or exceed state-of-the-art performance at a substantially lower computational cost. This tool offers a categorically different approach to connectome estimation that can augment how we extract connectivity information at scale. Our method is generalizable and may be extended to biomedical imaging problems such as tracing the bronchial trees in lungs or road networks in natural images. Copy rights belong to original authors. Visit the link for more info

Nicola talks about his long road into robotics and how BlueBotics handles indoor navigation and integrates it in automated guided vehicles (AGV).Like many, Nicola started out tinkering when he was young, and then got interested in computer science as he wanted to understand it better.Nicola gives us an overview of indoor navigation and its challenges. He shares a number of interesting projects, including professional cleaning and intralogistics in hospitals. We also find out what someone who wants use indoor navigation and AGV should think about.This podcast was recorded in 2016 and part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at richard@wevolver.com

The Mars 2020 mission is facing the most challenging landing yet on the Red Planet.

The Mars 2020 mission is facing the most challenging landing yet on the Red Planet.

Yiannis Rizopoulos speaks with Luca Verre, founder of the startup Chronocam, which develops a unique artificial vision and cybervision technology to be used for autonomous navigation and new IoT applications. Interviewed by Yiannis Rizopoulos for Tech Talks Central.

A JPL experiment is showing promise in giving future planetary rovers a chance to choose their own exploration targets

A JPL experiment is showing promise in giving future planetary rovers a chance to choose their own exploration targets