Section: New Results

Control of single and multiple Unmanned Aerial Vehicles

Single UAV

Participant : Paolo Robuffo Giordano.

Over the last years the robotics community witnessed an increasing interest in the Unmanned Aerial Vehicle (UAV) field. In particular quadrotor UAVs have become more and more widespread in the community as experimental platform for, e.g., testing novel 3D planning, control and estimation schemes in real-world indoor and outdoor conditions. Indeed, in addition to being able to take-off and land vertically, quadrotors can reach high angular accelerations thanks to the relatively long lever arm between opposing motors. This makes them more agile than most standard helicopters or similar rotorcraft UAVs, and thus very suitable to realize complex tasks such as aerial mapping, air pollution monitoring, traffic management, inspection of damaged buildings and dangerous sites, as well as agricultural applications such as pesticide spraying.

Despite these clear advantages, a clear shortcoming of the quadrotor design lies in its inherent underactuation (only 4 actuated propellers for the 6 dofs of the quadrotor pose). This underactuation limits the quadrotor flying ability in free or cluttered space and, furthermore, it also degrades the possibility of interacting with the environment by exerting desired forces in arbitrary directions. In [24] , a novel design for a quadrotor UAV with tilting propellers which is able to overcome these limitations has been presented and experimentally validated. Indeed, the additional set of 4 control inputs actuating the propeller tilting angles can be shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully-actuated flying vehicle and to overcome the aforementioned underactuation problem.

Furthermore, the issue of estimating online the UAV self-motion from vision has been considered. To this end, a novel nonlinear estimation scheme able to recover the metric UAV linear velocity from the scaled one obtained from the decomposition of the optical flow has been proposed in [15] . The observability conditions (in terms of persistency of excitation) needed to ensure a converging estimation have also been studied. The reported experimental results confirmed the effectiveness of the estimation scheme in recovering a reliable and accurate estimation of the UAV self-motion (linear and angular velocities) in realistic conditions.

This work has been realized in collaboration with the Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

Collective control of multiple UAVs

Participants : Fabrizio Schiano, Paolo Robuffo Giordano.

The challenge of coordinating the actions of multiple robots is inspired by the idea that proper coordination of many simple robots can lead to the fulfilment of arbitrarily complex tasks in a robust (to single robot failures) and highly flexible way. Teams of multi-robots can take advantage of their number to perform, for example, complex manipulation and assembly tasks, or to obtain rich spatial awareness by suitably distributing themselves in the environment. Within the scope of robotics, autonomous search and rescue, firefighting, exploration and intervention in dangerous or inaccessible areas are the most promising applications.

In the context of multi-robot (and multi-UAV) coordinated control, connectivity of the underlying graph is perhaps the most fundamental requirement in order to allow a group of robots accomplishing common goals by means of decentralized solutions. In fact, graph connectivity ensures the needed continuity in the data flow among all the robots in the group which, over time, makes it possible to share and distribute the needed information. However, connectivity alone is not sufficient to perform certain tasks when only relative sensing is used. For these systems, the concept of rigidity provides the correct framework for defining an appropriate sensing and communication topology architecture. Rigidity is a combinatorial theory for characterizing the “stiffness” or “flexibility” of structures formed by rigid bodies connected by flexible linkages or hinges. In a broader context, rigidity turns out to be an important architectural property of many multi-agent systems when a common inertial reference frame is unavailable. Applications that rely on sensor fusion for localization, exploration, mapping and cooperative tracking of a target, all can benefit from notions in rigidity theory. The concept of rigidity, therefore, provides the theoretical foundation for approaching decentralized solutions to the aforementioned problems using distance measurement sensors, and thus establishing an appropriate framework for relating system level architectural requirements to the sensing and communication capabilities of the system.

In [26] , a decentralized gradient-based rigidity maintenance action for a group of quadrotor UAVs has been proposed and tested in real experimental conditions. By starting in a rigid configuration, the group of UAVs is able to estimate their relative position from sole relative distance measurements, and then use these estimated relative positions in a control action able to preserve rigidity of the whole formation despite presence of sensor limitations (maximum range and line-of-sight occlusions), possible collisions with obstacles and inter-robot collisions. Furthermore, in [52] the novel case of bearing rigidity for directed graphs has been considered: here, rather than distances the measurements are the 3D bearing vectors expressed in the local body-frame of each agent. The theory has been developed for the case of planar agents in $SE\left(2\right)$ and a `scale-free' bearing controller has been proposed, able to steer the robot group towards a desired bearing formation.

These works were realized in collaboration with the robotics group at the Max Planck Institute for Biological Cybernetics, Tübingen, Germany and with Technion, Israel.

Cooperative localization using interval analysis

Participants : Vincent Drevelle, Ide Flore Kenmogne Fokam.

In the context of multi-robot fleets, cooperative localization consists in gaining better position estimate through measurements and data exchange with neighboring robots. Positioning integrity (i.e., providing reliable position uncertainty information) is also a key point for mission-critical tasks, like collision avoidance. The goal of this work is to compute position uncertainty volumes for each robot of the fleet, using a decentralized method (i.e using only local communication with the neighbors). The problem is addressed in a bounder-error framework, with interval analysis and constraint propagation methods. These methods enable to provide guaranteed position error bounds, assuming bounded-error measurements. They are not affected by over-convergence due to data incest, which makes them a well sound framework for decentralized estimation. Encouraging results have already been obtained for multi-robot underwater positioning with acoustical range measurements. Ongoing work focuses on cooperative localization in a multi-UAV fleet with image-based measurements (bearings).