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.
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.
Recent innovations around the autonomous car have shaken up the automotive industry. Manufacturers and their suppliers are all accelerating their work on the cars of the future, both regular human-operated cars as well as driverless or semi-autonomous vehicles. But beyond just issues of autonomy, these cars of the future are undergoing a fundamental shift in human-machine interaction. Consumers today crave more relational and conversational interactions with devices, as evidenced by the popularity of chatbots and virtual assistants like Siri and Alexa – and the automotive industry has taken notice. As such, next-generation cars are emerging as advanced artificial intelligence (AI) systems that will power an entirely new automotive experience in which cars will become conversational interfaces between the driver, passengers, the vehicle itself and its controls -- all connected to the IoT and mobile devices we use.
That's because, to paraphrase Amazon's Jeff Bezos, artificial intelligence (AI) is "not just in the first inning of a long baseball game, but at the stage where the very first batter comes up." Look around, and you will find AI everywhere--in self driving cars, Siri on your phone, online customer support, movie recommendations on Netflix, fraud detection for your credit cards, etc. To be sure, there's more to come. Featuring 30 lectures, MIT's course "introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence." It includes interactive demonstrations designed to "help students gain intuition about how artificial intelligence methods work under a variety of circumstances."