Real-time Model Predictive Control for Aerial Manipulation

Doctoral thesis (2018)

The rapid development in the field of Unmanned Aerial Vehicles (UAVs) is driven by new applications in agriculture, logistics, inspection and smart manufacturing. The future keys in these domains are the ... [more ▼]

The rapid development in the field of Unmanned Aerial Vehicles (UAVs) is driven by new applications in agriculture, logistics, inspection and smart manufacturing. The future keys in these domains are the abilities to autonomously interact with the environment and with other robotic systems. This thesis is providing control engineering solutions to contribute to these key capabilities. The first step of this thesis is to develop an understanding of the dynamic behavior of UAVs. For this purpose, dynamic and kinematic models are presented to describe a UAV's motion. This includes a kinematic model which is suitable for off-the-shelf UAVs and combines full 360° heading operation with a low computational complexity. The presented models are subsequently used to develop a nonlinear model predictive control NMPC strategy. In this context, the performance of several NMPC solvers and inequality constraint handling techniques is evaluated. The real-time capability and NMPC performance are validated with real AR.Drone 2.0 and DJI M100 quadrotors. This includes collision avoidance and advanced tracking scenarios. The design work-flow for the related control objectives and constraints is presented accordingly. As a next step, this UAV NMPC strategy is extended for a UAV with attached robotic arm. For this purpose, the forward kinematics of the robotic arm are developed and combined with the kinematic model of the UAV. The resulting NMPC strategy is validated in a grasping scenario with a real aerial manipulator. The final step of this thesis is the NMPC of cooperating UAVs. The computational complexity of such scenarios conflicts directly with the fast UAV dynamics. In addition, control objectives and system topologies can dynamically change. To address these challenges, this thesis presents the DENMPC software framework. DENMPC provides a computationally efficient central NMPC strategy that allows changing the control scenario at runtime. This is finally stated in the control of a real cooperative aerial manipulation scenario. [less ▲]

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

in Autonomous Robots (2018)

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 ... [more ▼]

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. [less ▲]

Area exploration with a swarm of UAVs combining deterministic Chaotic Ant Colony Mobility with position MPC

in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, July 27)

The recent advances in Unmanned Aerial Vehicles (UAVs) technology permit to develop new usages for them. One of the current challenges is to operate UAVs as an autonomous swarm. In this domain we already ... [more ▼]

The recent advances in Unmanned Aerial Vehicles (UAVs) technology permit to develop new usages for them. One of the current challenges is to operate UAVs as an autonomous swarm. In this domain we already proposed a new mobility model using Ant Colony Algorithms combined with chaotic dynamics (CACOC) to enhance the coverage of an area by a swarm of UAVs. In this paper we propose to consider this mobility model as waypoints for real UAVs. A control model of the UAVs is deployed to test the efficiency of the coverage of an area by the swarm. We have tested our approach in a realistic robotics simulator (V-Rep) which is connected with ROS. We compare the performance in terms of coverage using several metrics to ensure that this mobility model is efficient for real UAVs. [less ▲]

Model predictive control for cooperative control of space robots

in Model predictive control for cooperative control of space robots (2017, January)

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 ... [more ▼]

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. [less ▲]

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

in 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) (2017)

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 ... [more ▼]

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. [less ▲]

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

in IEEE Multi-Conference on Systems and Control (MSC 2016), Buenos Aires, Argentina, 2016 (2016, September 20)

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 ... [more ▼]

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. [less ▲]

A Modularization Approach for Nonlinear Model Predictive Control of Distributed Fast Systems

in 24th Mediterranean Conference on Control and Automation (MED), Athens, Greece, June 21-24, 2016 (2016, June 22)

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 ... [more ▼]

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. [less ▲]