Indoor operation of unmanned aerial vehicles (UAV s) poses many challenges due to the lack of GPS signal and cramped spaces. The presence of obstacles in an unfamiliar environment requires reliable state estimation and active algorithms to prevent collisions. In this paper, we present a teleoperated quadrotor UAV platform equipped with an onboard miniature computer and a minimal set of sensors for this task. The platform is capable of highly accurate state-estimation, tracking of desired velocity commanded by the user and ensuring collision-free navigation. The robot estimates its linear velocity through a Kalman filter integration of inertial and optical flow (OF) readings with corresponding distance measurements. An RGB-D camera serves the purpose of providing visual feedback to the operator and depth measurements to build a probabilistic, robo-centric obstacle model, allowing the robot to avoid collisions. The platform is thoroughly validated in experiments in an obstacle rich environment.

In this paper, we propose and experimentally verify a distributed formation control algorithm for a group of multirotor unmanned aerial vehicles (UAVs). The algorithm brings the whole group of UAVs simultaneously to a prescribed submanifold that determines the formation shape in an asymptotically stable fashion in two- and three-dimensional environments. The complete distributed control framework is implemented with the combination of a fast model predictive control method executed at 50 Hz on low-power computers onboard multirotor UAVs and validated via a series of hardware-in-the-loop simulations and real-robot experiments. The experiments are configured to study the control performance in various formation cases of arbitrary time-varying (e.g., expanding, shrinking, or moving) shapes. In the actual experiments, up to four multirotors have been implemented to form arbitrary triangular, rectangular, and circular shapes drawn by the operator via a human-robot interaction device. We also carry out hardware-in-the-loop simulations using up to six onboard computers to achieve spherical formations and a formation moving through obstacles.

Physical human-UAV interaction (PHUI) with Unmanned Aerial Vehicles (UAV) has many possible applications, and a few recent works [1], [2] have shown that it is possible to perform PHUI with a standard quadrotor. However, the intrinsic underactuation of quadrotors may hinder the interaction task and also cause safety issues. In this paper, we present an admittance control-based scheme to perform PHUI with a fully actuated UAV. The system also benefits from the robustness provided by our fully actuated UAV implementation of the adaptive super twisting controller (ASTC). While validating our system in simulation, we also show the superior performance of the fully actuated UAV with respect to a standard quadcopter.

In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robot's velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.