Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Facilitating Classroom Economics Experiments with an Emerging Technology: The Case of Clickers

The authors discuss how they used the audience response system (ARS) to facilitate pit market trading in an applied microeconomics class and report the efficacy of the approach. Using the ARS to facilitate active learning by engaging students in economics experiments has pedagogical advantages over both the labor-intensive approach of pencil-and-paper and the capital-intensive route of relying on networked or on-line computer labs which oftentimes preclude or restrict face-to-face student interactions. Thus, the new method of conducting experiments represents an added advantage on top of such conventional functions as taking attendance and administering quizzes of this increasingly popular classroom technology.Teaching/Communication/Extension/Profession,

Facilitating Classroom Economics Experiments with an Emerging Technology: The Case of Clickers

The audience response system (ARS) has increasingly been used to engage students by eliciting and analyzing responses to questions posed by instructors. The authors discuss how they used the system to facilitate pit market trading in a microeconomics class, report the efficacy of the approach and provide suggestions extending the use of ARS to other experiments. Using the ARS to facilitate active learning by engaging students in economics experiments has pedagogical advantages over both the labor-intensive approach of pencil-and-paper and the capital-intensive route of relying on networked or on-line computer labs which oftentimes preclude or restrict face-to-face student interactions. Thus, the new method represents an added advantage on top of such conventional functions as taking attendance and administering quizzes of this increasingly popular classroom technology.Teaching/Communication/Extension/Profession,

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

The Internet of Things Connectivity Binge: What are the Implications?

Despite wide concern about cyberattacks, outages and privacy violations, most experts believe the Internet of Things will continue to expand successfully the next few years, tying machines to machines and linking people to valuable resources, services and opportunities

A Fuzzy set-based method to identify the car position in a road lane at intersections by smartphone GPS data

Abstract Intelligent transportation systems (ITS) work by collections of data in real time. Average speed, travel time and delay at intersections are some of the most important measures, often used for monitoring the performance of transportation systems, and useful for system management and planning. In urban transportation planning, intersections are usually considered critical points, acting as bottlenecks and clog points for urban traffic. Thus, detecting the travel time at intersections in different turning directions is an activity useful to improve the urban transport efficiency. Smartphones represent a low-cost technology, with which is possible to obtain information about traffic state. However, smartphone GPS data suffer for low precision, mainly in urban areas. In this paper, we present a fuzzy set-based method for car positioning identification within road lanes near intersections using GPS data coming from smartphones. We have introduced the fuzzy sets to take into account uncertainty embedded in GPS data when trying to identify the position of cars within the road lanes. Moreover, we introduced a Genetic Algorithm to calibrate the fuzzy parameters in order to obtain a novel supervised clustering technique. We applied the proposed method to one intersection in the urban road network of Bari (Italy). First results reveal the effectiveness of the proposed methodology when comparing the outcomes of the proposed method with two well-known clustering techniques (Fuzzy C-means, K-means)

Geographically distributed real-time co-simulation of electric vehicle

The present paper shows the capabilities of a distributed real-time co-simulation environment merging simulation models and testing facilities for developing and verifying electric vehicles. This environment has been developed in the framework of the XILforEV project and the presented case is focused on a ride control with a real suspension installed on a test bench in Spain, which uses real-time information from a complete vehicle model in Germany. Given the long distance between both sites, it has been necessary to develop a specific delay compensation algorithm. This algorithm is general enough to be used in other real-time co-simulation frameworks. In the present work, the system architecture including the communication compensation is described and successfully experimentally validated

Grenoble Traffic Lab: An experimental platform for advanced traffic monitoring and forecasting

International audienceThis paper describes the main features of the "Grenoble Traffic Lab" (GTL), a new experimental platform for the collection of traffic data coming from a dense network of wireless sensors installed in the south ring of Grenoble, in France. The main challenges related to the configuration of the platform and data validation are discussed, and two relevant traffic monitoring and forecasting applications are presented to illustrate the operation of GTL

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version