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In this paper, we showcase a multi-robot design studio where simulation containers are browser accessible Lubuntu desktops. Our simulation testbed, based on ROS, Gazebo, PX4 flight stack has been developed to tackle higherlevel challenging tasks such as mission planning, vision-based problems, collision avoidance, and multi-robot coordination for Unpiloted Aircraft Systems (UAS). The novel architecture is built around TurboVNC and noVNC WebSockets technology, to seamlessly provide real-time web performance for 3D rendering in a collaborative design tool. We have built upon our previous work that leveraged concurrent multi-UAS simulations, and extended it to be useful for underwater, airship and ground vehicles. This opens up the possibility for both rigorous Monte Carlo styled software testing of heterogeneous swarm simulations, as well as sampling-based optimization of mission parameters. The new OpenUAV architecture has native support for ROS, PX4 and QGroundControl. Two case studies in the paper illustrate the development of UAS missions in the latest OpenUAV setup. The first example highlights the development of a visual-servoing technique for UAS descent on a target. Second case study referred to as terrain relative navigation (TRN) involves creating a reactive planner for UAS navigation by keeping a constant distance from the terrain.

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This paper discusses the development and refinement of several distributed matrix multiplication algorithms. Our goal in this research has been to determine if successful distribution of this problem is possible within a loosely-coupled environment. Our criteria for success are fast execution speed and, to a lesser extent, memory efficiency.Our results indicate that, perhaps counter-intuitively, it is possible to use distribution to improve the performance of dense matrix multiplication. The speed increase obtained ranges up to a factor of four, depending upon the algorithm and the process configuration used. Among the factors affecting performance are computational complexity, number and size of interprocess messages, and bookkeeping overhead. We conclude that this approach to matrix multiplication has potential. Furthermore, some of the principles discussed here may be usefully employed in the distribution of other algorithms of the same ad) computational complexity, such as LU decomposition (linear system solvers) and Cholesky factorization.