Miguel Angel Olivares Mendez

A modularization approach for nonlinear model predictive control of distributed fast systems

Distributed interconnected systems are omnipresent today. The development of advanced control methods for such systems are still challenging. Herein, the real-time applicability, flexibility, portability and ease of implementation are issues of the existing control solutions, especially for more advanced methods such as model predictive control. This paper is addressing these issues by presenting an efficient modular composition scheme for distributed fast nonlinear systems. The advantage of this modularization approach is the capability of changing control objectives, constraints, dynamics and system topology online while maintaining fast computation. This work analyzes the functions that have to be provided for a continuation generalized minimal residual method (CGMRES) model predictive controller based on the underlying control problem. The specific structure of these functions allows their decomposition into suitable fast modules. These modules are then used to recompose the functions which are required for the control of distributed systems in a computational efficient way, while maintaining the flexibility to dynamically exchange system parts. To validate this computational efficiency, the computation time of the proposed modular control approach is compared with a standard nonmodular implementation in a pursuit scenario of quadrotor unmanned aerial vehicles (UAV). Furthermore the real-time applicability is discussed for the given scenario.

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.Drone2 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.

Rapid Prototyping Framework for Visual Control of Autonomous Micro Aerial Vehicles

Rapid prototyping environments can speed up the research of visual control algorithms. We have designed and implemented a software framework for fast prototyping of visual control algorithms for Micro Aerial Vehicles (MAV). We have applied a combination of a proxy-based network communication architecture and a custom Application Programming Interface. This allows multiple experimental configurations, like drone swarms or distributed processing of a drone’s video stream. Currently, the framework supports a low-cost MAV: the Parrot AR.Drone. Real tests have been performed on this platform and the results show comparatively low figures of the extra communication delay introduced by the framework, while adding new functionalities and flexibility to the selected drone. This implementation is open-source and can be downloaded from www.vision4uav.com/?q=VC4MAV-FW

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.

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.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.

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 (TPC), 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.

Visual odometry based absolute target geo-location from micro aerial vehicle

An unmanned aerial system capable of finding world coordinates of a ground target is proposed here. The main focus here was to provide effective methodology to estimate ground target world coordinates using aerial images captured by the custom made micro aerial vehicle (MAV) as a part of visual odometery process on real time. The method proposed here for finding targets ground coordinates uses a monocular camera which is placed in MAV belly in forward looking/ Downward looking mode. The Binary Robust Invariant Scalable Key points (BRISK) algorithm was implemented for detecting feature points in the consecutive images. After robust feature point detection, efficiently performing Image Registration between the aerial images captured by MAV and with the Geo referenced images is the prime and core computing operation considered. Precise Image alignment is implemented by accurately estimating Homography matrix. In order to accurately estimate Homography matrix which consists of 9 parameters, this algorithm solves the problem in a Least Square Optimization way. Therefore, this framework can be integrated with visual odometery pipeline; this gives the advantage of reducing the computational burden on the hardware. The system can still perform the task of target geo-localization efficiently based on visual features and geo referenced reference images of the scene which makes this solution to be found as cost effective, easily implementable with robustness in the output. The hardware implementation of MAV along with this dedicated system which can do the proposed work to find the target coordinates is completed. The main application of this work is in search and rescue operations in real time scenario. The methodology was analyzed and executed in MATLAB before implementing real time on the developed platform. Finally, three case studies with different advantages derived from the proposed framework are represented.