A new consensus theory-based method for formation control and obstacle avoidance of UAVs

Abstract

Unmanned aerial vehicle (UAV) formation has a wide range of applications as it can increase the success rate of task and improve the reliability of system. Formation control and obstacle avoidance are two related key problems and are studied based on the consensus theory in this paper. First, two forms of kinetic models for UAV are described, and the standard model and the model with autopilot are deployed to apply in obstacle avoidance and formation control respectively. The constraints on the maneuverability of UAV are proposed. Based on the standard consensus algorithm, the three degrees of freedom (DOFs) of UAV in the improved consensus algorithm (ICA) are converted to be consistent with the DOFs describing the relative positions among UAVs, which make it possible to add the formation information to the standard consensus algorithm. The constraints on the maneuverability of UAV are also met by using the proposed minimal adjustment strategy. In the obstacle avoidance problem, the particle swarm optimization (PSO) algorithm is introduced, and the static obstacles and dynamic obstacles are dealt with by the ICA-PSO algorithm and the model predictive control (MPC)-PSO algorithm respectively, and the two algorithms can be combined to cope with more complicated situations. Simulation results demonstrate that the ICA can make UAVs with different initial states form the specified formation while satisfying all the constraints, and a safe and efficient flight for UAV formation can be ensured with the obstacle avoidance algorithm.