This paper presents the first worldwide functional prototype omnidirectional multi-rotor aerial vehicle with fixed uni-directional thrusters, with an on-board power source. An optimization algorithm computes the positions and orientations of the propellers in the body frame of the prototype to achieve the omnidirectional capability, while minimizing the platform's weight and the required thrust to hover at any orientation, in addition to other construction requirements. The effect of the aerodynamic interaction between the different propellers is identified experimentally, and the ensuing results are included in the optimization algorithm to avoid such interactions during flight. The prototype's performance is assessed in real experiments demonstrating the decoupling between the forces and moments of the drone, its ability to track concurrently independent positions and orientations, and its ability to hover at a fixed position while rotating.

Autonomous aerial vehicles play a critical role in search and rescue operations, where navigation through cluttered and confined environments is essential. To this end, this paper presents a novel trajectory planning framework for omnidirectional drones that dynamically adjusts tracking velocity based on the platform’s proximity to obstacles, ensuring a balance between safety and efficiency in cluttered and challenging environments. The proposed approach generates a geometric path to the target location. At each waypoint, the minimum distance between the drone’s convex hull and surrounding obstacles is determined, allowing the computation of the velocity constraints. By slowing down near obstacles and accelerating in open spaces, the method enhances both safety and maneuverability. The framework is validated through real-world experiments using the OmniOcta UAV, demonstrating its ability to navigate through constrained spaces. Furthermore, we present an experimental study to investigate key sources of tracking deviations, including propeller dynamics and aerodynamic interactions near obstacles.

In this paper we propose to control a quadrotor through direct acceleration feedback. The proposed method, while simple in form, alleviates the need for accurate estimation of platform parameters such as mass and propeller effectiveness. In order to use efficaciously the noisy acceleration measurements in direct feedback, we propose a novel regression-based filter that exploits the knowledge on the commanded propeller speeds, and extracts smooth platform acceleration with minimal delay. Our tests show that the controller exhibits a few millimeter error when performing real world tasks with fast changing mass and effectiveness, e.g., in pick and place operation and in turbulent conditions. Finally, we benchmark the direct acceleration controller against the PID strategy and show the clear advantage of using high-frequency and low-latency acceleration measurements directly in the control feedback, especially in the case of low frequency position measurements that are typical for real outdoor conditions.

We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as Deep-MoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to navigate to the target location. We conduct extensive experiments to assess the components of our network and prove their necessity to imitate humans. Our experiments show that DeepMoTIion outperforms all the benchmarks in terms of human imitation, achieving a 24% reduction in time series-based path deviation over the next best approach. In addition, while many other approaches often failed to reach the target, our method reached the target in 100% of the test cases while complying with social norms and ensuring human safety.