A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems

Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests

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

Combining Occupancy Grids with a Polygonal Obstacle World Model for Autonomous Flights

This chapter presents a mapping process that can be applied to autonomous systems for obstacle avoidance and trajectory planning. It is an improvement over commonly applied obstacle mapping techniques, such as occupancy grids. Problems encountered in large outdoor scenarios are tackled and a compressed map that can be sent on low-bandwidth networks is produced. The approach is real-time capable and works in full 3-D environments. The efficiency of the proposed approach is demonstrated under real operational conditions on an unmanned aerial vehicle using stereo vision for distance measurement

Vision-based Testbeds For Control System Applicaitons

In the field of control systems, testbeds are a pivotal step in the validation and improvement of new algorithms for different applications. They provide a safe, controlled environment typically having a significantly lower cost of failure than the final application. Vision systems provide nonintrusive methods of measurement that can be easily implemented for various setups and applications. This work presents methods for modeling, removing distortion, calibrating, and rectifying single and two camera systems, as well as, two very different applications of vision-based control system testbeds: deflection control of shape memory polymers and trajectory planning for mobile robots. First, a testbed for the modeling and control of shape memory polymers (SMP) is designed. Red-green-blue (RGB) thresholding is used to assist in the webcam-based, 3D reconstruction of points of interest. A PID based controller is designed and shown to work with SMP samples, while state space models were identified from step input responses. Models were used to develop a linear quadratic regulator that is shown to work in simulation. Also, a simple to use graphical interface is designed for fast and simple testing of a series of samples. Second a robot testbed is designed to test new trajectory planning algorithms. A templatebased predictive search algorithm is investigated to process the images obtained through a lowcost webcam vision system, which is used to monitor the testbed environment. Also a userfriendly graphical interface is developed such that the functionalities of the webcam, robots, and optimizations are automated. The testbeds are used to demonstrate a wavefront-enhanced, Bspline augmented virtual motion camouflage algorithm for single or multiple robots to navigate through an obstacle dense and changing environment, while considering inter-vehicle conflicts, iv obstacle avoidance, nonlinear dynamics, and different constraints. In addition, it is expected that this testbed can be used to test different vehicle motion planning and control algorithms

Aeronautical Engineering. A continuing bibliography with indexes, supplement 156

This bibliography lists 288 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1982

Test bed for applications of heterogeneous unmanned vehicles

Abstract This article addresses the development and implementation of a test bed for applications of heterogeneous unmanned vehicle systems. The test bed consists of unmanned aerial vehicles (Parrot AR.Drones versions 1 or 2, Parrot SA, Paris, France, and Bebop Drones 1.0 and 2.0, Parrot SA, Paris, France), ground vehicles (WowWee Rovio, WowWee Group Limited, Hong Kong, China), and the motion capture systems VICON and OptiTrack. Such test bed allows the user to choose between two different options of development environments, to perform aerial and ground vehicles applications. On the one hand, it is possible to select an environment based on the VICON system and LabVIEW (National Instruments) or robotics operating system platforms, which make use the Parrot AR.Drone software development kit or the Bebop_autonomy Driver to communicate with the unmanned vehicles. On the other hand, it is possible to employ a platform that uses the OptiTrack system and that allows users to develop their own applications, replacing AR.Drone’s original firmware with original code. We have developed four experimental setups to illustrate the use of the Parrot software development kit, the Bebop Driver (AutonomyLab, Simon Fraser University, British Columbia, Canada), and the original firmware replacement for performing a strategy that involves both ground and aerial vehicle tracking. Finally, in order to illustrate the effectiveness of the developed test bed for the implementation of advanced controllers, we present experimental results of the implementation of three consensus algorithms: static, adaptive, and neural network, in order to accomplish that a team of multiagents systems move together to track a target. Keywords Test bed, heterogeneous vehicles, laboratory environment