Robust Trajectory Generation and Control for Quadrotor Motion Planning with Field-of-View Control Barrier Certification

TL;DR

This paper introduces a real-time, communication-free distributed control method for multi-robot systems that maintains visual contact and robustness using control barrier functions and model predictive control.

Contribution

It presents a novel continuous-time robust trajectory generation and control approach certified by control barrier functions, along with an efficient MPC-CBF optimization procedure.

Findings

The method ensures robustness to tracking loss and communication failure.

It stabilizes visual contact through control Lyapunov-barrier functions.

The approach is computationally efficient with sequential quadratic programming.

Abstract

Many approaches to multi-robot coordination are susceptible to failure due to communication loss and uncertainty in estimation. We present a real-time communication-free distributed navigation algorithm certified by control barrier functions, that models and controls the onboard sensing behavior to keep neighbors in the limited field of view for position estimation. The approach is robust to temporary tracking loss and directly synthesizes control to stabilize visual contact through control Lyapunov-barrier functions. The main contributions of this paper are a continuous-time robust trajectory generation and control method certified by control barrier functions for distributed multi-robot systems and a discrete optimization procedure, namely, MPC-CBF, to approximate the certified controller. In addition, we propose a linear surrogate of high-order control barrier function constraints and use sequential quadratic programming to solve MPC-CBF efficiently.