Simulation platforms are critical and indispensable tools for application developments of unmanned aerial vehicles (UAVs) because the UAVs are generally costly, have certain requirements for the test environment, and need professional licensed operators. Thus, developers prefer (or have) to test their applications on simulation platforms before implementing them on real machines. In the past decades, a considerable number of simulation platforms for robots have been developed, which brings convenience to developers, but also makes them hard to choose a proper one as they are not always familiar with all the features of platforms. To alleviate this dilemma, this paper provides a survey of open-source simulation platforms and employs the simulation of a multi-copter UAV swarm as an example. The survey covers seven widely used simulators, including Webots, Gazebo, CoppeliaSim, ARGoS, MRDS, MORSE, and USARSim. The paper outlines the requirements for multi-copter UAV swarms and shows how to select an appropriate platform. Additionally, the paper presents a case study of a UAV swarm based on Webots. This research will be beneficial to researchers, developers, educators, and engineers who seek suitable simulation platforms for application development, (not only multi-copter UAV swarms but also other types of robots), which further helps them to save expenses for testing, and speed up development progress.

Reasonable semantic partition of indoor areas can improve space utilization, optimize property management, and enhance safety and convenience. Existing algorithms for such partitions have drawbacks, such as the inability to consider semantics, slow convergence, and sensitivity to outliers. These limitations make it difficult to have partition schemes that can match the real-world observations. To obtain proper partitions, this paper proposes an improved K-means clustering algorithm (IK-means), which differs from traditional K-means in three respects, including the distance measurement method, iterations, and stop conditions of iteration. The first aspect considers the semantics of the spaces, thereby enhancing the rationality of the space partition. The last two increase the convergence speed. The proposed algorithm is validated in a large-scale indoor scene, and the results show that it has outperformance in both accuracy and efficiency. The proposed IK-means algorithm offers a promising solution to overcome existing limitations and advance the effectiveness of indoor space partitioning algorithms. This research has significant implications for the semantic area partition of large-scale and complex indoor areas, such as shopping malls and hospitals.