Collision avoidance for multiple MAVs using fast centralized NMPC
This addresses collision avoidance for MAVs in multi-agent systems, but it appears incremental as it builds on existing NMPC methods with a specific optimization algorithm.
The paper tackled collision avoidance for multiple micro aerial vehicles (MAVs) by proposing a centralized nonlinear model predictive control (CNMPC) scheme, achieving fast convergence and constraint satisfaction in simulations with comparisons based on computation time and violations relative to agent count.
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 constrains 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, while are provided with respect to the number of agents.