Nonlinear Model Predictive Control on SE(3) for Quadrotor Trajectory Tracking and Obstacle Avoidance Some recent contributions have emerged designing Nonlinear Model Predictive Control (NMPC) for UAVs. However, these approaches often split the problem into upper and inner layers, or attitude and position control, separately, which can be undesirable in complex tasks such as those involving optimal functional cost depending on position and attitude references simultaneously. Moreover, most of their controlleru0027s design does not handle the avoidance of representational singularities. Therefore, the present work proposes a NMPC formulated on the Special Euclidean group SE(3), which has a single optimization layer, for quadrotor safe trajectory tracking with obstacle avoidance capacity. A numerical experiment illustrates this proposal and is used to evaluate the controlleru0027s performance.

Fast Trajectory Planning for Multiple Quadrotors using Relative Safe Flight Corridor This paper presents a new trajectory planning method for multiple quadrotors in obstacledense environments. We suggest a relative safe flight corridor (RSFC) to model safe region between a pair of agents, and it is used to generate linear constraints for intercollision avoidance by utilizing the convex hull property of relative Bernstein polynomial. Our approach employs a graphbased multiagent pathfinding algorithm to generate an initial trajectory, which is used to construct a safe flight corridor (SFC) and RSFC. We express the trajectory as a piecewise Bernstein polynomial and formulate the trajectory planning problem into one quadratic programming problem using linear constraints from SFC and RSFC. The proposed method can compute collisionfree trajectory for 16 agents within a second and for 64 agents less than a minute, and it is validated both through simulation and indoor flight test.