This paper presents a novel actuation model and control allocation strategy for a redundantly-actuated multirotor unmanned aerial vehicle (UAV), referred to as the omnicopter. With an unconventional configuration, the omnicopter's eight propellers are able to produce all the six components of net force/torque, with two degrees of actuation redundancy. This enables the vehicle to execute motion trajectories unattainable with conventional underactuated multi-rotors. A new inverse actuator model is proposed that accounts for the significant interactions between propeller airflows by relating their output thrust forces to their input motor commands. Actuation redundancy is resolved by solving a convex constrained optimization problem. Its solution yields the most power efficient set of propellers thrusts that would produce a required net force/torque, while respecting the propeller thrust limits. When the required force/torque is infeasible due to the thrust limits, the solution would minimize the norm of the error between the desired and actual net force/torque vectors. Experimental results demonstrate the effectiveness of the proposed model and control allocation strategy.

A provably stable decentralized control scheme is proposed to allow multiple conventional quadcopters carry a cable-suspended payload. The method exploits a fundamental energetic passivity property of the combined drones, cables, and payload system to stably move the payload from its origin to destination. This is achieved without making any assumption about the status of the cables tension during the flight, and any measurement from the payload. The controller is decentralized in the sense that inter-drone communication of feedback measurements is not required. Motion stability is demonstrated via a Lyapunov analysis. The proposed controller is successfully implemented on a three-drone payload transport system in an indoor environment, using measurement from an optical tracking systems and the drones on-board IMUs.

This paper is concerned with the mechanical design of a mobile manipulating unmanned ground vehicle (MM-UGV) coupled with an existing commercial unmanned aerial vehicle (UAV) to create a novel hybrid aerial/ground mobile manipulator. A novel systematic optimization-based approach is presented for making important design choices, such as the selection of gearboxes and electric DC motors in the drive-train, manipulator link lengths, and UGV base length. The objective is to minimize the overall mass of the MM-UGV. Constraints related to workspace, dynamic tip-over stability, actuator torque/force limits in static and dynamic motions, executed in the air or on the ground, and battery properties are incorporated into the problem formulation. The system operating conditions in the form of the range of end-effector forces, operating surface grade, and various position, velocity and acceleration variables are provided by the designer. The resulting problem is a robust bilevel nonlinear optimization, in which some of the constraints are derived from maximization/minimization over the operational variables to ensure constraint satisfaction in all possible operation scenarios. The problem is solved using a genetic algorithm. Numerical simulations demonstrate that the proposed strategy produces a design that meets the user-specified requirements.

This paper deals with motion control of under-actuated aerial robotic manipulators composed of multi-rotor Unmanned Aerial Vehicles and multi-link serial robotic arms. Kinematics and dynamics analysis of the multi-body mechanical system are performed. Using the method of virtual decomposition, adaptive motion control laws are proposed based on rigid-body inverse dynamics with feedback compensation. Underactuation imposes second-order nonholonomic constraints on the system dynamics, which are taken into account in the control algorithm. System stability and convergence of the tracking errors are proven using a Lyapunov analysis. A simulation case study demonstrates the effectiveness of the proposed model-based controller compared to a conventional controller.