Distributed Model Predictive Control for Collision and Obstacle Avoidance of Multiple Quadcopters
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As the cost to manufacture quadcopters decrease, multi-agent applications for civilian tasks, such as large-scale surveying, search and rescue missions and fire fighting, are becoming increasingly realizable. However, a multi-agent system of fast moving quadcopters has a high risk of collisions with neighbouring quadcopters or obstacles. The objective of this work is to develop a control strategy for collision and obstacle avoidance of multiple quadcopters. In this thesis, the problem of distributed model predictive control (MPC) for collision avoidance among a team of multiple quadcopters attempting to reach consensus is investigated. Violations of a predetermined safety radius generates output constraints on the MPC optimization function. In addition, logarithmic barrier functions are implemented as input rate constraints on the control actions. Extensive simulation studies for a team of four quadcopters illustrate promising results of the proposed control strategy and case variations. In addition, distributed MPC parameter effects on the system performance are studied and a successful isolated study for obstacle avoidance of static objects is presented.