Testbed

About: Testbed is a research topic. Over the lifetime, 10858 publications have been published within this topic receiving 147147 citations. The topic is also known as: test bed.

OpenUAV: a UAV testbed for the CPS and robotics community

Abstract: Multirotor Unmanned Aerial Vehicles (UAV) have grown in popularity for research and education, overcoming challenges associated with fixed wing and ground robots. Unfortunately, extensive physical testing can be expensive and time consuming because of short flight times due to battery constraints and safety precautions. Simulation tools offer a low barrier to entry and enable testing and validation before field trials. However, most of the well-known simulators today have a high barrier to entry due to the need for powerful computers and the time required for initial set up. In this paper, we present OpenUAV, an open source test bed for UAV education and research that overcomes these barriers. We leverage the Containers as a Service (CaaS) technology to enable students and researchers carry out simulations on the cloud. We have based our framework on open-source tools including ROS, Gazebo, Docker, PX4, and Ansible, we designed the simulation framework so that it has no special hardware requirements. Two use-cases are presented. First, we show how a UAV can navigate around obstacles, and second, we test a multi-UAV swarm formation algorithm. To our knowledge, this is the first open-source, cloud-enabled testbed for UAVs. The code is available on GitHub: https://github.com/Open-UAV.

A neural network based distributed intrusion detection system on cloud platform

Abstract: Intrusion detection system (IDS) is an important component to maintain network security. Also, as the cloud platform is quickly evolving and becoming more popular in our everyday life, it is useful and necessary to build an effective IDS for the cloud. However, existing intrusion detection techniques will be likely to face challenges when deployed on the cloud platform. The pre-determined IDS architecture may lead to overloading of a part of the cloud due to the extra detection overhead. This paper proposes a neural network based IDS which is a distributed system with an adaptive architecture so as to make full use of the available resources without overloading any single machine in the cloud. Moreover, with the machine learning ability from the neural network, the proposed IDS can detect new types of attacks with fairly accurate results. Evaluation of the proposed IDS with the KDD dataset on a physical cloud testbed shows that it is a promising approach to detecting attacks in the cloud infrastructure.

Experimental characterization of an 802.11b wireless mesh network

Abstract: Wireless Mesh Networks are being deployed everywhere as an alternative to broadband connections. Their ease of setup and large coverage are attractive attributes. However, there are very few performance studies of mesh networks especially in the multiple channel arena. Our objective is to study the performance and characterize the 802.11b wireless mesh backbone as a linear topology with respect to multiple channel usage. We look at the relative performances of single and multiple channels, as well as the number of hops utilized. We introduce a number of communication flows into the network to study the interactions. Finally, we look at the physical placement of the antennas on an access point to determine its impact on performance. Several design decisions for the configuration of wireless mesh network deployments have been inferred from our experimental testbed.

The formation control testbed

Abstract: Terrestrial planet finder (TPF) is a space telescope mission which performs spectral analysis of the infrared emissions from extrasolar planets, and which searches for carbon-based life on such planets. One configuration being considered for this mission is a stellar interferometer with several collectors and a combiner on separate spacecraft flying in a tightly controlled formation. The distance to earth for this mission are sufficiently great that having ground in the loop for reconfiguration or collision avoidance maneuvers impractical. Moreover, because of constraints in the orientation of the spacecraft relative to the sun, limitations on the field of view of relative range and bearing sensors, and restrictions on the orientations of thrusters, both the attitude and the relative position of each spacecraft in the formation must be taken into account in the event of a temporary sensing or control fault during maneuvers. These maneuvers include initial deployment of the formation, reconfiguration, and collision avoidance maneuvers. The formation algorithms and simulation testbed (FAST) and the formation control testbed (FCT) at JPL are being built to simulate and demonstrate 6 degree of freedom, autonomous formation flying and reconfiguration for TPF. The testbeds are complementary. Control algorithms simulated in the FAST are tested in the FCT in order to validate the FAST. This paper describes the design and construction of the formation control testbed. The FCT consists of three robots navigating on an air bearing floor, propelled by cold gas thrusters. Each robot contains an attitude platform supported on a spherical air bearing which provides three rotational degrees of freedom. The sixth degree of freedom, vertical translation, is provided by a powered vertical stage, actively controlled to provide a simulated zero-g environment for the attitude platform.