ROS Based High Performance Control Architecture for an Aerial Robotic Testbed

The purpose of this thesis is to show the development of an aerial testbed based on the Robot Operating System (ROS). Such a testbed provides flexibility to control heterogenous vehicles, since the robots are able to simply communication with each other on the High Level (HL) control side. ROS runs on an embedded computer on-board each quadrotor. This eliminates the need of a Ground Base Station, since the complete HL control runs on-board the Unmanned Aerial Vehicle (UAV). The architecture of the system is explained throughout the thesis with detailed explanations of the specific hardware and software used for the system. The implementation on two different quadrotor models is documented and shows that even though they have different components, they can be controlled similarly by the framework. The user is able to control every unit of the testbed with position, velocity and/or acceleration data. To show this independency, control architectures are shown and implemented. Extensive tests verify their effectiveness. The flexibility of the proposed aerial testbed is demonstrated by implementing several applications that require high-performance control. Additionally, a framework for a flying inverted pendulum on a quadrotor using robust hybrid control is presented. The goal is to have a universal controller which is able to swing-up and balance an off-centered pendulum that is attached to the UAV linearly and rotationally. The complete dynamic model is derived and a control strategy is presented. The performance of the controller is demonstrated using realistic simulation studies. The realization in the testbed is documented with modifications that were made to the quadrotor to attach the pendulum. First flight tests are conducted and are presented. The possibilities of using a ROS based framework is shown at every step. It has many advantages for implementation purposes, especially in a heterogeneous robotic environment with many agents. Real-time data of the robot is provided by ROS topics and can be used at any point in the system. The control architecture has been validated and verified with different practical tests, which also allowed improving the system by tuning the specific control parameters

ViSpec: A graphical tool for elicitation of MTL requirements

One of the main barriers preventing widespread use of formal methods is the elicitation of formal specifications. Formal specifications facilitate the testing and verification process for safety critical robotic systems. However, handling the intricacies of formal languages is difficult and requires a high level of expertise in formal logics that many system developers do not have. In this work, we present a graphical tool designed for the development and visualization of formal specifications by people that do not have training in formal logic. The tool enables users to develop specifications using a graphical formalism which is then automatically translated to Metric Temporal Logic (MTL). In order to evaluate the effectiveness of our tool, we have also designed and conducted a usability study with cohorts from the academic student community and industry. Our results indicate that both groups were able to define formal requirements with high levels of accuracy. Finally, we present applications of our tool for defining specifications for operation of robotic surgery and autonomous quadcopter safe operation.Comment: Technical report for the paper to be published in the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems held in Hamburg, Germany. Includes 10 pages and 19 figure

Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning

In this paper, we investigate the operation of an aerial manipulator system, namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with two degrees of freedom to carry out actuation tasks on the fly. Our solution is based on employing a Q-learning method to control the trajectory of the tip of the arm, also called end-effector. More specifically, we develop a motion planning model based on Time To Collision (TTC), which enables a quadrotor UAV to navigate around obstacles while ensuring the manipulator's reachability. Additionally, we utilize a model-based Q-learning model to independently track and control the desired trajectory of the manipulator's end-effector, given an arbitrary baseline trajectory for the UAV platform. Such a combination enables a variety of actuation tasks such as high-altitude welding, structural monitoring and repair, battery replacement, gutter cleaning, skyscrapper cleaning, and power line maintenance in hard-to-reach and risky environments while retaining compatibility with flight control firmware. Our RL-based control mechanism results in a robust control strategy that can handle uncertainties in the motion of the UAV, offering promising performance. Specifically, our method achieves 92% accuracy in terms of average displacement error (i.e. the mean distance between the target and obtained trajectory points) using Q-learning with 15,000 episode

Model Predictive Control for Micro Aerial Vehicles: A Survey

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations