Autonomous Construction of Multiple Structures Using Learning Automata: Description and Experimental Validation

In this paper, we develop an adaptive scheme based on reinforcement learning (RL) for planning the construction tasks using a quadrotor. Moreover, an autonomous construction system to assemble user-specified 3-D structures is proposed. Nowadays, complex construction tasks using mobile robots are characterized by three fundamental problems: assembly planning, motion planning, and path tracking control. The high-level plan to perform the construction task consists of assembly mode algorithms that are derived offline in a simulation environment through learning and heuristic search. A promising approach to design and optimize the path tracking controllers for a quadrotor as well as the attitude controllers using RL is presented. This paper describes a comprehensive validation framework that enables an aerial robot to build structures in a robust and safe manner. The experimental trials for building the 3-D structures using the designed high-level plans and path tracking controllers have provided encouraging results.