Path planning for multi-copter UAVs using tutorial training and self learning inspired teaching-learning-based optimization

Abstract

Route preparation for drones is a complex method to achieve an optimal path and meet constraints following specific tasks. This paper addresses the problem of a planning method for a multi-copter unmanned aerial vehicle (UAV) to examine ground surfaces. A multi-objective route planning algorithm, named the tutorial training and self learning inspired teaching learning-based optimization (TS-TLBO), is then introduced to create a feasible and flyable path while avoiding obstacles. Here, we first select a joint cost function that includes different constraints to improve operational safety, at the same time, to meet task requirements. The path-tracking scheme is then applied in the quadcopter to verify the proposed approach. Experiment results show the workability of our proposed path planning process.