Planning and learning for cooperative construction task with quadrotors

In this paper, we describe a stochastic learning approach for planning of assembly and construction tasks of 3-D structures using multiple quadrotors. A planning framework is proposed to generate different sets of high-level plans for the aerial robots. This architecture demonstrates significant advances in ability to quickly find good solutions for complex construction tasks, considering the real world criteria. The high-level plans are derived off-line using learning and heuristic search algorithms in a simulation environment. This process involves the planning of the sequence of maneuvers for each aerial robot, the sequence of assembly of the desired structure, and the set of trajectories for the quadrotors navigate through the moderately constrained and dynamic environment. Moreover, an efficient conflict resolution for multiple vehicles based on speed planning is proposed. The simulation results of the autonomous aerial robot construction system are presented and the obtained high-level plans are evaluated.