Collision Avoidance for Multiple Micro Aerial Vehicles using Fast Centralized Nonlinear Model Predictive Control⁎This work has been partially funded by the European Unions Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 730302 SIMS. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Lindqvist, Björn; Mansouri, Sina Sharif; Sopasakis, Pantelis; Nikolakopoulos, George
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 9303
• Page End: 9309
• Publication Date: 2020
• Subjects: Aerial navigation -- MAV -- Model predictive control -- Collision avoidance -- Obstructed navigation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2384
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This article proposes a novel control architecture using a centralized nonlinear model predictive control (CNMPC) scheme for controlling multiple micro aerial vehicles (MAVs). The control architecture uses an augmented state system to control multiple agents and performs both obstacle and collision avoidance. The optimization algorithm used is OpEn, based on the proximal averaged Newton type method for optimal control (PANOC) which provides fast convergence for non-convex optimization problems. The objective is to perform position reference tracking for each individual agent, while nonlinear constraints guarantee collision avoidance and smooth control signals. To produce a trajectory that satisfies all constraints a penalty method is applied to the nonlinear constraints. The efficacy of this proposed novel control scheme is successfully demonstrated through simulation results and comparisons, in terms of computation time and constraint violations, which are provided with respect to the number of agents.