In this paper we analyze the equilibrium points of a collaborative transportation task, composed of two unmanned aerial vehicles and a payload - in this case, a bar. Moreover, centralized and decentralized linear model predictive controllers are designed, where the nonlinear dynamics are linearized around the equilibrium points previously analyzed. A comparison between the centralized and decentralized formulations is provided, based on experimental results for both setups, and considering the time to solution and performance of each controller. Our findings provide new operational equilibrium points that can be paired with predictive model-based controllers for efficient operation.

Cooperative robotics is a trending topic nowadays as it makes possible a number of tasks that cannot be performed by individual robots, such as heavy payload transportation and agile manipulation. In this work, we address the problem of cooperative transportation by heterogeneous, manipulator- endowed robots. Specifically, we consider a generic number of robotic agents simultaneously grasping an object, which is to be transported to a prescribed set point while avoiding obstacles. The procedure is based on a decentralized leader-follower Model Predictive Control scheme, where a designated leader agent is responsible for generating a trajectory compatible with its dynamics, and the followers must compute a trajectory for their own manipulators that aims at minimizing the internal forces and torques that might be applied to the object by the different grippers. The Model Predictive Control approach appears to be well suited to solve such a problem, because it provides both a control law and a technique to generate trajectories, which can be shared among the agents. The proposed algorithm is implemented using a system comprised of a ground and an aerial robot, both in the robotic Gazebo simulator as well as in experiments with real robots, where the methodological approach is assessed and the controller design is shown to be effective for the cooperative transportation task.

This paper studies the problem of Image-Based Visual Servo Control (IBVS) for quadrotors. Although the control of quadrotors has been extensively studied in the last decades, combining the IBVS module with the quadrotor's dynamics is still hard, mainly due to the under-actuation issues related to the quadrotor control as opposed to the 6 DoF control outputs generated by the IBVS modules. We propose an alternative formulation to solve this problem, by particularly using linear Model Predictive Control (MPC), that allows us to relax the UAVs under-actuation issues. Stability guarantees of the proposed scheme are presented. The proposed model is validated with synthetic data and tested in a real UAV's setup.

We consider a system composed of a bar tethered to two unmanned aerial vehicles (UAVs), where the cables behave as rigid links under tensile forces, and with the control objective of stabilizing the bar's pose around a desired pose. Each UAV is equipped with a PID control law, and we verify that the bar's motion is decomposable into three decoupled motions, namely a longitudinal, a lateral and a vertical. We then provide relations between the UAV s' gains, which, if satisfied, allows us to decompose each of those motions into two cascaded motions; the latter relations between the UAV s' gains are found so as to counteract the system asymmetries, such as the different cable lengths and the different UAV s' weights. Finally, we provide conditions, based on the system's physical parameters, that describe good and bad types of asymmetries. We present experiments that demonstrate the stabilization of the bar's pose.

Recently, we developed the Parallel Heterogeneous Architecture Constraint Toolkit (PHACT), which is a multi-threaded constraint solver capable of using all the available devices which are compatible with OpenCL, in order to speed up the constraint satisfaction process. In this article, we introduce an evolution of PHACT which includes the ability to execute FlatZinc and MiniZinc models, as well as architectural improvements which boost the performance in solving CSPs, especially when using GPUs.

This paper presents the design of a small aerial robot for inhabited microgravity environments, such as orbiting space stations (e.g., ISS). In particular, we target a fleet of robots, called Space CoBots, for collaborative tasks with humans, such as telepresence and cooperative mobile manipulation. The design is modular, comprising an hexrotor based propulsion system, and a stack of modules including batteries, cameras for navigation, a screen for telepresence, a robotic arm, space for extension modules, and a pair of docking ports. These ports can be used for docking and for mechanically attaching two Space CoBots together. The kinematics is holonomic, and thus the translational and the rotational components can be fully decoupled. We employ a multi-criteria optimization approach to determine the best geometric configuration for maximum thrust and torque across all directions. We also tackle the problem of motion control: we use separate converging controllers for position and attitude control. Finally, we present simulation results using a realistic physics simulator. These experiments include a sensitivity evaluation to sensor noise and to unmodeled dynamics, namely a load transportation.