Robust 3D Distributed Formation Control With Collision Avoidance And Application To Multirotor Aerial Vehicles

Abstract

We present a distributed control strategy for a team of quadrotors to autonomously achieve a desired 3D formation. Our approach is based on local relative position measurements and does not require global position information or inter-vehicle communication. We assume that quadrotors have a common sense of direction, which is chosen as the direction of gravitational force measured by their onboard IMU sensors. However, this assumption is not crucial, and our approach is robust to inaccuracies and effects of acceleration on gravitational measurements. In particular, converge to the desired formation is unaffected if each quadrotor has a velocity vector that projects positively onto the desired velocity vector provided by the formation control strategy. We demonstrate the validity of proposed approach in an experimental setup and show that a team of quadrotors achieve a desired 3D formation.

Invariant Set Distributed Explicit Reference Governors for Provably Safe On-Board Control of Nano-Quadrotor Swarms

“…Most approaches, such as (Campos-Macías et al, 2017;Chen et al, 2017;Herbert et al, 2017;Preiss et al, 2017a;Wang et al, 2017;Fridovich-Keil et al, 2018;Honig et al, 2018;Kolaric et al, 2018;Cappo et al, 2018a;Cappo et al, 2018b;Xu and Sreenath, 2018;Bajcsy et al, 2019;Du et al, 2019;Fathian et al, 2019;Liu et al, 2019;Luis and Schoellig, 2019;Rubies-Royo et al, 2019;Vukosavljev et al, 2019), try to ensure a particular level of safety and robustness, by running the core search-based or optimization-based algorithms off-board the UAVs, and thus outsource the high computational cost to ground control stations that send the trajectories to the UAV's on-board position or attitude controller. Frameworks such as (Preiss et al, 2017a;Honig et al, 2018) combine graph-based planning and continuous trajectory optimization to compute safe and smooth trajectories, but take several minutes for a swarm of hundreds of quadrotors in obstacle-rich environments.…”

“…Distributed formation control approaches that have been demonstrated on small quadrotors, but are computed off-board have shown robustness to bounded measurement noise (Kolaric et al, 2018), to communication delays, nonlinearities, parametric perturbations, and external disturbances (Liu et al, 2019). Input feasibility and collision avoidance is guaranteed in (Fathian et al, 2019) for single-integrator dynamics, and is claimed to be extendable to agents with higher-order dynamics in (Fathian et al, 2018).…”

“…The main challenge that arises from the control problem stated in Section 4.3 is that it combines the nonlinear dynamics of the individual agent with the nonconvex constraints of the aggregated swarm. The higher-order nonlinear agent dynamics (3) would be significantly easier to stabilize in the absence of constraints, whereas the position constraints ( 8)-( 10) would be easier to enforce if the agent dynamics were a first-order linear system _ p i ρ i as in (Fathian et al, 2019). We propose a multi-layer control architecture that relies on the ERG framework (Nicotra and Garone, 2018) and decouples the control problem into more tractable sub-tasks to facilitate on-board implementation.…”

Collision Avoidance of SDRE Controller using Artificial Potential Field Method: Application to Aerial Robotics

“…The left arm is placed at ( , 0) d  and the right arm at ( , 0) d , with the assumption of symmetric structure and the same kinematics and dynamics parameters. The gravity, 2 9.81 (m/s ) g  , is present in Y direction. .…”

“…Defining a penalty for collisions in the fitness function for UAV trajectory optimization was proposed for free path planning [8]. Fathian et al presented robust threedimensional distributed formation guidance with collision avoidance and application to multirotor aerial vehicles [9]. The presented research used a path generator for the first loop and a tracker for the second one.…”