This paper presents a methodology for mapping and localization of Unmanned Aerial Vehicles (UAVs) based on the integration of sensors from different modalities. Particularly, we integrate distance estimations to Ultra-Wideband (UWB) sensors and 3D point-clouds from RGB-D sensors. First, a novel approach for environment mapping is introduced, exploiting the synergies between UWB sensors and point-clouds to produce a multi-modal 3D map that integrates the estimated UWB sensors position. This map is further integrated into a Monte Carlo Localization method to robustly estimate the UAV pose. Finally, the full approach is tested with real indoor flights and validated with a motion tracking system.

In this paper a three layer control architecture for multiple aerial robotic manipulators is presented. The top layer, on the basis of the desired mission, determines the end-effector desired trajectory for each manipulator, while the middle layer is in charge of computing the motion references in order to track such end-effectors trajectories coming from the upper layer. Finally the bottom layer is a low level motion controller, which tracks the motion references. The overall mission is decomposed in a set of elementary behaviors which are combined together, through the Null Space-based Behavioral (NSB) approach, into more complex compounds behaviors. The proposed framework has been tested conducting an experimental campaign.

In this paper, an impedance control scheme for aerial robotic manipulators is proposed, with the aim of reducing the end-effector interaction forces with the environment. The proposed control has a multi-level architecture, in detail the outer loop is composed by a trajectory generator and an impedance filter that modifies the trajectory to achieve a complaint behaviour in the end-effector space; a middle loop is used to generate the joint space variables through an inverse kinematic algorithm; finally the inner loop is aimed at ensuring the motion tracking. The proposed control architecture has been experimentally tested.

This work tackles the problem of controlling an Unmanned Aerial Vehicle equipped with a robotic Manipulator and it has been developed within the framework of the EU-funded ARCAS (Aerial Robotics Cooperative Assembly System) project. A behavioral control, based on the Null Space-based Behavioral (NSB) paradigm, is proposed to tackle the coordination between the arm and vehicle motions. To this aim, a set of basic functionalities (called elementary behaviors) are designed and combined in a priority order to attain complex tasks (called compound behaviors). The proposed controller has been experimentally validated on a multirotor aircraft with an attached 6 Degree of Freedoms manipulator. Two experimental case studies, involving several compound behaviors, have been reported and the results show the effectiveness of the approach.

A multilayer architecture to control multirotor UAVs equipped with a servo robot arm is proposed in this paper. The main purpose is to control the aerial platform taking into account the presence of the moving manipulator. Three layers are considered in this work. First, a novel mechanism is proposed considering a moving battery to counterweight the statics of the robotic arm. Then, in order to overcome the mechanical limitations of the previous layer, the residual of the arm static effects on the UAV is computed and compensated through the given control thrust and torques. Finally, an estimator of external forces and moments acting on the aerial vehicle is considered and the estimations are fed back to the controller to compensate neglected aerodynamic effects and the arm dynamics. The performance of the proposed architecture has been experimentally evaluated.

Landing of unmanned helicopters is a challenging maneuver which can be seriously affected by GPS sensor reliability. This work explores an alternative approach to GPS sensors to avoid typical drawbacks, such as inaccuracies of standalone sensors or weight/cost for Real Time Kinematic (RTK) setups. To this end, the paper studies a setup consisting of a tether linking the helicopter to the landing point. Appropriate sensing of tether attitude relative to the helicopter together with an altitude measurement, allows estimation of the helicopter's linear position relative to the landing point. Additionally, the tension exerted on the tether provides a stabilizing effect on helicopter translational dynamics. Taking into account all these considerations, a model-based control strategy is developed and implemented in the real platform. The experimental results endorse the validity of the proposed approach.

In this paper, we introduce an approach called FSBS (Forward Search in Belief Space) for online planning in POMDPs. The approach is based on the RTBSS (Real-Time Belief Space Search) algorithm of [1]. The main departure from the algorithm is the introduction of similarity measures in the belief space. By considering statistical divergence measures, the similarity between belief points in the forward search tree can be computed. Therefore, it is possible to determine if a certain belief point (or one very similar) has been already visited. This way, it is possible to reduce the complexity of the search by not expanding similar nodes already visited in the same depth. This reduction of complexity makes possible the real-time implementation of more complex problems in robots. The paper describes the algorithm, and analyzes different divergence measures. Benchmark problems are used to show how the approach can obtain a ten-fold reduction in the computation time for similar obtained rewards when compared to the original RTBSS. The paper also presents experiments with a quadrotor in a search application.

This paper presents an efficient conflict resolution method for multiple aerial vehicles based on speed planning. The problem is assigning a speed profile to each aerial vehicle in real time such that the separation between them is greater than a minimum safety value and the total deviation from the initial planned trajectories is minimized. Also, the arrival time of each aerial vehicle at the end waypoint of the trajectory is taken into account to solve the conflicts. The proposed method involves the use of appropriate airspace discretization. The method consists of two steps: a search tree step, which finds if it exists a solution; and an optimization step by solving a QP-problem, which minimizes a cost function. The paper also presents simulations for several scenarios and experiments that have been carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

In this paper we extend our previous work where the mean of the global cost was used as a performance metric for distributed task allocation algorithms. In this case, we move a step forward and calculate the variance of the global cost. This second parameter gives us a better understanding of the distributed algorithm performance, i.e., we can estimate how much the algorithm behavior diverts from its mean. The normal distribution, computed from the theoretical mean and variance, is shown to be suitable for modeling the global cost. This approximation enables us to compare our algorithm theoretically in different cases.

In this paper, we present a distributed market-based algorithm called S+T, which solves the multi-robot task allocation (MRTA) problem in applications that require the cooperation among the robots to accomplish all the tasks. If a robot cannot execute a task by itself, it asks for help and, if possible, another robot will provide the required service. In the paper, tasks consisting in transmitting data in real-time that could require communication relay services are considered. On the other hand, the parameters of the algorithm can be adapted to give priority to either the execution time or the energy consumption in the mission. The potential generation of deadlocks associated to the relation between tasks and services is studied, and as an original result, a distributed algorithm that prevent them is proposed. The algorithm has been tested in simulations that illustrate the main features of the S+T algorithm.

The multi-robot task allocation (MRTA) problem has become a key research topic in the field of distributed multirobot coordination in recent years. In this paper, two algorithms for the distributed solution of the MRTA problem are presented. In our market-based approach, robots consider their local plans when bidding and multiple tasks can be allocated to a single robot during the negotiation process. The second algorithm described in the paper is based on the negotiation of subset of tasks and can be considered as a generalization of the first one, which only negotiates single tasks. Both algorithms have been tested in a multirobot simulator with multiple missions consisting in visiting waypoints with promising results.

In this paper, an analysis of a market-based approach applied to the initial formation problem is presented. This problem tries to determine which mobile sensor should go to each position of a desired formation in order to minimize an objective. In our case, this objective is the global distance traveled by all the mobile sensors. In this analysis, a bound on the efficiency for the market-based algorithm is calculated and it is shown that the relative difference as compared with the optimal solution increases with the logarithm of the total number of mobile sensors. The theoretical results are validated with numerous simulations.