Iterative Decentralized Planning for Collective Construction Tasks with Quadrotors

This paper describes an iterative decentralized planning and learning method, based on stochastic learning automata theory and heuristic search techniques, to generate construction and motion strategies to build different types of three-dimensional structures using multiple quadrotors. This architecture is proposed to simultaneously solve three main problems: 1) the iterative generation of feasible construction and motion plans for each quadrotor; 2) the optimization with constraints on power and assembly while taking into account the dynamic nature of the environment, and 3) the planning of the translational speeds and selection of breakpoints for each vehicle. The quadrotors learn the optimal action policy to construct the structures while avoiding collisions during the loading and unloading procedures. In order to demonstrate the generality of the solution, simulated trials of the proposed autonomous construction system are presented where different three-dimensional structures are built.