We propose a method to compute optimal control paths for autonomous vehicles deployed for the purpose of inferring a velocity field. In addition to being advected by the flow, the vehicles are able to effect a fixed relative speed with arbitrary control over direction. It is this direction that is used as the basis for the locally optimal control algorithm presented here, with objective formed from the variance trace of the expected posterior distribution. We present results for linear flows near hyperbolic fixed points.
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Here's a taste of what's to come: In contrast to the usual approach to operating self-driving cars, we did not program any explicit object detection, mapping, path planning or control components into this car. Instead, the car learns on its own to create all necessary internal representations necessary to steer, simply by observing human drivers.
Search And Tracking (SAT) is the problem of searching for a mobile target and tracking it after it is found. As this problem has important applications in search-and-rescue and surveillance operations, recently there has been increasing interest in equipping unmanned aerial vehicles (UAVs) with autonomous SAT capabilities. State-of-the-art approaches to SAT rely on estimating the probability density function of the target's state and solving the search control problem in a greedy fashion over a short planning horizon (typically, a one-step lookahead). These techniques suffer high computational cost, making them unsuitable for complex problems. In this paper, we propose a novel approach to SAT, which allows us to handle big geographical areas, complex target motion models and long-term operations. Our solution is to track the target reactively while it is in view and to plan a recovery strategy that relocates the target every time it is lost, using a high-performing automated planning tool. The planning problem consists of deciding where to search and which search patterns to use in order to maximise the likelihood of recovering the target. We show experimental results demonstrating the potential of our approach.
Over the past few years the CES trade show has become a familiar post-holidays pilgrimage for many of the country's biggest marketers. They see the event as a way to get a sneak peek at the latest tech gadgets and technologies that can help them engage with their customers. This year marketing executives from companies such as Coca-Cola, Unilever, Johnson & Johnson, Campbell Soup and PepsiCo Inc. made their way to Las Vegas for the gathering. The convention was jam-packed with everything from self-driving cars to robots that play chess to Procter & Gamble's air-freshener spray that can connect with Alphabet Inc.'s Nest home to automatically release pleasant scents in the home. But there was one category that seemed to especially win over marketers: virtual assistants.