Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation

Abstract

Non-linear model predictive control (nMPC) is a powerful approach to control complex robots (such as humanoids, quadrupeds or unmanned aerial manipulators (UAMs)) as it brings important advantages over other existing techniques. The full-body dynamics, along with the prediction capability of the optimal control problem (OCP) solved at the core of the controller, allows to actuate the robot in line with its dynamics. This fact enhances the robot capabilities and allows, e.g., to perform intricate maneuvers at high dynamics while optimizing the amount of energy used. Despite the many similarities between humanoids or quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to UAMs.This paper provides a thorough description of how to use such techniques in the field of aerial manipulation. We give a detailed explanation of the different parts involved in the OCP, from the UAM dynamical model to the residuals in the cost function. We develop and compare three different nMPC controllers: Weighted MPC, Rail MPC and Carrot MPC, which differ on the structure of their OCPs and on how these are updated at every time step. To validate the proposed framework, we present a wide variety of simulated case studies. First, we evaluate the trajectory generation problem, i.e., optimal control problems solved offline, involving different kinds of motions (e.g., aggressive maneuvers or contact locomotion) for different types UAMs. Then, we assess the performance of the three nMPC controllers, i.e., closed-loop controllers solved online, through a variety of realistic simulations. For the benefit of the community, we have made available the source code related to this work.

Autonomous aerial robotics for package delivery: A technical review

“…Rigid linked manipulators, as shown in Figure 7, are either a series (Martí-Saumell et al, 2021) or parallel (Bodie et al, 2021) | 11 depend on the configuration state of the entire system (Ruggiero et al, 2018). Most approaches tackle this by taking either a centralized (Heredia et al, 2014) or decentralized formulation (Chaikalis et al, 2020).…”

Autonomous Aerial Delivery Vehicles, a Survey of Techniques on how Aerial Package Delivery is Achieved

“…state are 0, in other words modeling steady state [Yang et al, 2014]. Other popular control algorithms include Sliding Mode controller [Jiao et al, 2018, Kuchwa-Dube and Pedro, 2018, Bouzgou et al, 2020, Guayasamin et al, 2018, Model Predictive Control [Emami and Banazadeh, 2021, Martí-Saumell et al, 2021, Lunni et al, 2017, Garimella and Kobilarov, 2015, and Proportional Integral and Differential (PID) based [Luna et al, 2018, Ghadiok et al, 2011, Meng et al, 2017. Rigid link based manipulators face many challenges which has been discussed.…”