SwarmLab: a Matlab Drone Swarm Simulator

Among the available solutions for drone swarm simulations, we identified a gap in simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis, and do not require the user to interface with multiple programming languages. We present SwarmLab, a software entirely written in Matlab, that aims at the creation of standardized processes and metrics to quantify the performance and robustness of swarm algorithms, and in particular, it focuses on drones. We showcase the functionalities of SwarmLab by comparing two state-of-the-art algorithms for the navigation of aerial swarms in cluttered environments, Olfati-Saber's and Vasarhelyi's. We analyze the variability of the inter-agent distances and agents' speeds during flight. We also study some of the performance metrics presented, i.e. order, inter and extra-agent safety, union, and connectivity. While Olfati-Saber's approach results in a faster crossing of the obstacle field, Vasarhelyi's approach allows the agents to fly smoother trajectories, without oscillations. We believe that SwarmLab is relevant for both the biological and robotics research communities, and for education, since it allows fast algorithm development, the automatic collection of simulated data, the systematic analysis of swarming behaviors with performance metrics inherited from the state of the art.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

The influence of limited visual sensing on the Reynolds flocking algorithm

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which consists in the analysis of the effects of limited sensing on the behavior of the swarm. In nature, the central sensor modality often used for achieving flocking is vision. In this work, we study how the reduction in the field of view and the orientation of the visual sensors affect the performance of the Reynolds flocking algorithm used to control the swarm. To quantify the impact of limited visual sensing, we introduce different metrics such as (i) order, (ii) safety, (iii) union and (iv) connectivity. As Nature suggests, our results confirm that lateral vision is essential for coordinating the movements of the individuals. Moreover, the analysis we provide will simplify the tuning of the Reynolds flocking algorithm which is crucial for real-world deployment and, especially for aerial swarms, it depends on the envisioned application. We achieve the results presented in this paper through extensive Monte-Carlo simulations and integrate them with the use of genetic algorithm optimization

SwarmLab: a MATLAB Drone Swarm Simulator

Among the available solutions for drone swarm simulations, we identified a lack of simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis. Moreover, users who want to dive in the research field of drone swarms often need to interface with multiple programming languages. We present SwarmLab, a software entirely written in MATLAB, that aims at the creation of standardized processes and metrics to quantify the performance and robustness of swarm algorithms, and in particular, it focuses on drones. We showcase the functionalities of SwarmLab by comparing two decentralized algorithms from the state of the art for the navigation of aerial swarms in cluttered environments, Olfati-Saber’s and Vasarhelyi’s. We analyze the variability of the inter-agent distances and agents’ speeds during flight. We also study some of the performance metrics presented, i.e. order, inter- and extra-agent safety, union, and connectivity. While Olfati-Saber’s approach results in a faster crossing of the obstacle field, Vasarhelyi’s approach allows the agents to fly smoother trajectories, without oscillations. We believe that SwarmLab is relevant for both the biological and robotics research communities, and for education, since it allows fast algorithm development, the automatic collection of simulated data, the systematic analysis of swarming behaviors with performance metrics inherited from the state of the art

acados/acados: v0.2.5

<h2>What's Changed</h2> <ul> <li>update BLASFEO and HPIPM by @FreyJo in https://github.com/acados/acados/pull/969</li> <li>Fix typo in minimal_example_closed_loop.py by @Federico-PizarroBejarano in https://github.com/acados/acados/pull/970</li> <li>Fix dependency handling in CMake config file by @Hs293Go in https://github.com/acados/acados/pull/971</li> <li>Fix sfun sources if hessian is not exact by @asparc in https://github.com/acados/acados/pull/974</li> <li>update ROADMAP by @sandmaennchen in https://github.com/acados/acados/pull/964</li> <li>Work on solution sensitivities by @FreyJo in https://github.com/acados/acados/pull/975</li> <li>Implement cost integration via IRK for Convex-over-nonlinear cost by @FreyJo in https://github.com/acados/acados/pull/976</li> <li>Add time in IRK, allow to cost integration with time dependent function by @FreyJo in https://github.com/acados/acados/pull/977</li> <li>Documentation fixes by @FreyJo in https://github.com/acados/acados/pull/979</li> <li>Formulate constraints as L2 and Huber penalties in Python by @FreyJo in https://github.com/acados/acados/pull/980</li> </ul> <h2>New Contributors</h2> <ul> <li>@Federico-PizarroBejarano made their first contribution in https://github.com/acados/acados/pull/970</li> <li>@Hs293Go made their first contribution in https://github.com/acados/acados/pull/971</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/acados/acados/compare/v0.2.4...v0.2.5</p&gt

acados/acados: v0.2.6

<h2>What's Changed</h2> <ul> <li>fix CodeQL warnings by @FreyJo in https://github.com/acados/acados/pull/984</li> <li>fix condition for checking number of stages in detect_dims_ocp.m by @Ajin2305 in https://github.com/acados/acados/pull/989</li> <li>Codeql review by @FreyJo in https://github.com/acados/acados/pull/988</li> <li>Rework CasADi requirements in MEX interface by @FreyJo in https://github.com/acados/acados/pull/991</li> <li>Fix get_optimal_value_gradient, add getter for p from HPIPM by @FreyJo in https://github.com/acados/acados/pull/993</li> <li>Windows python interface by @asparc in https://github.com/acados/acados/pull/968</li> </ul> <h2>New Contributors</h2> <ul> <li>@Ajin2305 made their first contribution in https://github.com/acados/acados/pull/989</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/acados/acados/compare/v0.2.5...v0.2.6</p&gt