Small unmanned aerial vehicles (UAVs) have generally little capacity to carry payloads. Through collaboration, the UAVs can increase their joint payload capacity and carry more significant loads. For maximum flexibility to dynamic and unstructured environments and task demands, we propose a fully decentralized control infrastructure based on a swarm-specific scripting language, Buzz. In this paper, we describe the control infrastructure and use it to compare two algorithms for collaborative transport: field potentials and spring-damper. We test the performance of our approach with a fleet of micro-UAVs, demonstrating the potential of decentralized control for collaborative transport.
The same type of micro-UAVs used in our experiments, the Crazyflie, was subject to a detailed swarm controller design @cite_9 . We leverage the stability of their on-board control and the scalability of their configuration to deploy our solution. Nevertheless, collaborative payload transport has never been attempted with such a small and sensitive device. Other solutions for controlling a swarm of Crazyflies has been considered @cite_14 @cite_3 , but they are centralized. As with , their scripts are specific to their implementation for this platform, whereas we leveraged a domain-specific programming language for swarms @cite_4 to guarantee portability to other platforms.
@cite_9 @cite_14 @cite_4 @cite_3
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This work presents the design of an open-source control architecture specifically tailored to the rapid development and testing of control and coordination algorithms on micro quadrotors. The proposed design extends an existing open-source and open-hardware quadrotor project, i.e., the Crazyflie nano quadrotor, by adding the guidance, navigation and control layers required to accomplish autonomous flight. The control layer, in particular, is based on a cascade control algorithm able to globally stabilize the position and the attitude of the vehicle. The global property guaranteed by the algorithm allows the quadrotors to perform aggressive maneuvers. The guidance layer is designed to coordinate simultaneously multiple vehicles by generating suitable reference trajectories. Experiments demonstrate the effectiveness of the proposed design. This tutorial chapter will teach readers how to use ROS to fly a small quadcopter both individually and as a group. We will discuss the hardware platform, the Bitcraze Crazyflie 2.0, which is well suited for swarm robotics due to its small size and weight. After first introducing the crazyflie_ros stack and its use on an individual robot, we will extend scenarios of hovering and waypoint following from a single robot to the more complex multi-UAV case. Readers will gain insight into physical challenges, such as radio interference, and how to solve them in practice. Ultimately, this chapter will prepare readers not only to use the stack as-is, but also to extend it or to develop their own innovations on other robot platforms. Micro aerial vehicles have the potential to assist humans in tasks such as manipulation and transportation for construction and humanitarian missions, beyond simply acquiring data and building maps. In this letter, we address the state estimation, control, and trajectory planning in cooperative transportation of structures, which are either too heavy or too big to be carried by small microvehicles. Specifically, we consider small quadrotors, each equipped only with a single camera and inertial measurement unit as a sensor. The key contributions are 1) a new approach to coordinated control, which allows independent control of each vehicle while guaranteeing the system's stability and 2) a new cooperative localization scheme that allows each vehicle to benefit from measurements acquired by other vehicles. The latter relies on the vehicles exploiting the inherent rigid structure information to infer additional constraints between the vehicles' poses allowing us to formulate the pose estimation problem as an optimization problem on the Lie group SE(3). The proposed approach is validated through experimental results with multiple quadrotors grasping and transporting a rigid structure. Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.