Implementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems.

This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined with a low-computational load while being on-line adaptable to changes in the control scenario. Examples for such scenarios are cooperative control, task-based control and fault-tolerant control, where the system's topology, dynamics, objectives and constraints are changing. The framework combines a fast Nonlinear Model Predictive Control (NMPC), a communication interface with the Robot Operating System (ROS) as well as a modularization that allows an event-based change of the NMPC scenario. To experimentally validate performance and event-based adaptability of the framework, this paper is using a cooperative control scenario of Unmanned Aerial Vehicles (UAVs)

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints: a workflow to create sensor constraint based potential functions for the control of cooperative localization scenarios with mobile robots.

The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver

Model predictive control for spacecraft rendezvous.

The current paper addresses the problem of Spacecraft Rendezvous using Model Predictive Control (MPC). The Clohessy-Wiltshire-Hill equations are used to model the spacecraft relative motion. Here the rendezvous problem is discussed by trajectory control using MPC method. Two different scenarios are addressed in trajectory control. The first scenario consist of position control with fuel constraint, secondly the position control is performed in the presence of obstacles. Here the problem of fuel consumption and obstacle avoidance is addressed directly in the cost function. The proposed methods are successfully analysed through simulations

Collision avoidance effects on the mobility of a UAV swarm using chaotic ant colony with model predictive control.

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics

Cooperative localization of unmanned aerial vehicles in ROS - The Atlas node

This paper is presenting the implementation and experimental validation of the cooperative robot localization framework “Atlas”. For ease of application, Atlas is implemented as a package for the Robot Operating System (ROS). ATLAS is based on dynamic cooperative sensor fusion which optimizes the estimated pose with respect to noise, respective variance. This paper validates the applicability of Atlas by cooperatively localizing multiple real quadrotors using cameras and fiduciary markers

A tracking error control approach for model predictive position control of a quadrotor with time varying reference

In mobile robotic applications, a common problem is the following of a given trajectory with a constant velocity. Using standard model predictive control (MPC) for tracking of time varying trajectories leads to a constant tracking error. This problem is modelled in this paper as quadrotor position tracking problem. The presented solution is a computationally light-weight target position control (T PC), that controls the tracking error of MPCs for constantly moving targets. The proposed technique is assessed mathematically in the Laplace domain, in simulation, as well as experimentally on a real quadrotor system

A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors

Unmanned aerial vehicles (UAVs) are the future technology for autonomous fast transportation of individual goods. They have the advantage of being small, fast and not to be limited to the local infrastructure. This is not only interesting for delivery of private consumption goods up to the doorstep, but also particularly for smart factories. One drawback of autonomous drone technology is the high development costs, that limit research and development to a small audience. This work is introducing a position control with collision avoidance as a first step to make low-cost drones more accessible to the execution of autonomous tasks. The paper introduces a semilinear state-space model for a commercial quadrotor and its adaptation to the commercially available AR.Drone 2 system. The position control introduced in this paper is a model predictive control (MPC) based on a condensed multiple-shooting continuation generalized minimal residual method (CMSCGMRES). The collision avoidance is implemented in the MPC based on a sigmoid function. The real-time applicability of the proposed methods is demonstrated in two experiments with a real AR.Drone quadrotor, adressing position tracking and collision avoidance. The experiments show the computational efficiency of the proposed control design with a measured maximum computation time of less than 2ms

Control of Aerial Manipulation Vehicle in Operational Space

Operational Space Control of an Aerial Manipulation Vehicle is discussed here. The Aerial Manipulation Vehicle has a highly coupled dynamics due to the interaction between the Quadrotor and the attached manipulator. The nonlinear coupling introduces disturbances on the quadrotor which hinders precise control. A control solution in the operational space is considered where the End-Effector has to reach a final position starting from an initial hovering position. A hierarchical control approach is implemented where the outermost layer consist of Closed Loop Inverse Kinematics algorithm followed by position and attitude controlled loop for the quadrotor. The robotic arm and the quadrotor are controlled by different combinations of PID control methods. The proposed method is successfully tested through simulations for position control of the Aerial Manipulator

Model predictive control for cooperative control of space robots

The problem of Orbital Manipulation of Passive body is discussed here. Two scenarios including passive object rigidly attached to robotic servicers and passive body attached to servicers through manipulators are discussed. The Model Predictive Control (MPC) technique is briefly presented and successfully tested through simulations on two cases of position control of passive body in the orbit

Hierarchical control of aerial manipulation vehicle

Hierarchical Control of the Aerial Manipulator is treated here. The modelling aspect of the highly coupled Aerial Vehicle which includes Quadrotor and manipulator is discussed. The control design to perform tasks in operational space is addressed along with stability discussion. The simulation studies are successfully performed to validate the design methodology