The reconfigurable unmanned aerial vehicle (RUAV) has the ability to create and break physical links to self-assemble and self-disassemble in midair. For the changes in task or environment, this system can dynamically disassemble the rectangular structure into multiple individual UAV modules or integrate these UAV modules into a whole. For practical applications, the R-UAV requires collaborative decision-making for autonomous navigation in complex environments. However, the navigation problem of the R-UAV has not been investigated. In this paper, we propose a dynamic multi-agent deep deterministic policy gradient (DMADDPG) algorithm for autonomous navigation of R-UAV. This algorithm introduces the leader agent assignment mechanism and a collaborative experience reward. The former deals with the action conflict problem caused by the disappearance of the UAV agent when multiple UAV modules are assembled. The latter provides guidance for the UAV agent to plan a collision-free and efficient trajectory. We validate our strategy in both simulation and practical scenarios, and experimental results demonstrate that the proposed scheme can generate reasonable and efficient paths for R-UAV in the presence of obstacles. The experiment video is available at https://youtu.be/mVm0qCvB7HY.

In this paper, we introduce a composable and extensible drone with tilting rotors (CEDTR). We aimed for a function that could optimally match the load capacity, degree of freedom (DOF), speed and endurance with diverse mission requirements by changing the quantity and form of combinations. First, we propose a decentralized modular controller to allow a team of physically connected modules to fly cooperatively. Second, we divide all the combinations into three categories according to the different control methods. Three generalized control strategies are proposed to control both position and attitude independently by tilting the directions of the propellers. We carried out experiments to demonstrate the feasibility of this mechanical design and control method. The experiment video is available at https://youtu.be/7RvxiV4FPq4.