This paper proposes a method to robustify model predictive path integral (MPPI) control by directly taking into account the effects of parameter uncertainty into the controller formulation. Leveraging the recent notion of closed-loop state sensitivity, the proposed MPPI can consider the state sensitivity against parameter mismatch as a part of the system state, and consequently exploit this additional information to address the challenge of model mismatch in sampling-based model predictive control. Using an obstacle avoidance scenario, we demonstrate the use of our approach to control an aerial robot. We present an embedded implementation of our method, utilizing parallelization of computations on a GPU. Finally, we show the increased robustness of our approach over a standard MPPI controller through hardware-in-the-loop simulations and validate its embedded real-time properties.

This work considers a large class of systems composed of multiple quadrotors manipulating deformable and extensible cables. The cable is described via a discretized representation, which decomposes it into linear springs interconnected through lumped-mass passive spherical joints. Sets of flat outputs are found for the systems. Numerical simulations support the findings by showing cable manipulation relying on flatness-based trajectories. Eventually, we present an experimental validation of the effectiveness of the proposed discretized cable model for a two-robot example. Moreover, a closed-loop controller based on the identified model and using cable-output feedback is experimentally tested.

In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations (PDEs) combined with boundary conditions described by ordinary differential equations (ODEs). The proper orthogonal decomposition (POD) method is adopted to project the original infinite-dimensional system on a finite low-dimensional space spanned by orthogonal basis functions. Based on such a reduced order model, nonlinear model predictive control (NMPC) is implemented online to realize both position and shape trajectory tracking of the flexible cable in an optimal predictive fashion. The proposed POD-based reduced modeling and optimal control paradigms are verified in simulation using an accurate high-dimensional finite difference method- (FDM) based model and experimentally using a real quadrotor and a cable. The results show the viability of the POD-based predictive control approach (allowing to close the control loop on the full system state) and its superior performance compared to an optimally tuned PID controller (allowing to close the control loop on the quadrotor state only).

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm.

Offline optimal planning of trajectories for redundant robots along prescribed task space paths is usually broken down into two consecutive processes: first, the task space path is inverted to obtain a joint space path, then, the latter is parametrized with a time law. If the two processes are separated, they cannot optimize the same objective function, ultimately providing sub-optimal results. In this paper, a unified approach is presented where dynamic programming is the underlying optimization technique. Its flexibility allows accommodating arbitrary constraints and objective functions, thus providing a generic framework for optimal planning of real systems. To demonstrate its applicability to a real world scenario, the framework is instantiated for time-optimality on Franka Emika’s Panda robot. The well-known issues associated with the execution of non-smooth trajectories on a real controller are partially addressed at planning level, through the enforcement of constraints, and partially through post-processing of the optimal solution. The experiments show that the proposed framework is able to effectively exploit kinematic redundancy to optimize the performance index defined at planning level and generate feasible trajectories that can be executed on real hardware with satisfactory results.Note to Practitioners—The common planning algorithms which consolidated over the years for generating trajectories for non-redundant robots are not adequate to fully exploit the more advanced capabilities offered by redundant robots. This is especially true in performance-demanding tasks, as for robots employed on assembly lines in manufacturing industries, repeatedly performing the same activity. Once the assembly line engineer has defined the tool path in the task space, our planning algorithm unifies inverse kinematics and time parametrization so as to bring the manipulator at its physical limits to achieve specific efficiency goals, being execution time the most typical one. The algorithm is configurable in terms of constraints to consider and objective functions to optimize, therefore it can be easily adapted to optimize other custom-defined efficiency indices, to better respond to the needs of the automation plant. Being based on discrete dynamic programming, the global optimum is guaranteed for a given resolution of the problem. This can be configured by the operator to achieve the desired trade-off between efficiency and planning time. In our experiments, we go through the whole process of planning and executing a time-optimal trajectory on a real robot, and discuss some practical details, such as trajectory smoothness and actuator saturation, aiding the practitioners in deploying our algorithm effectively. Currently, the algorithm’s applicability is limited to those cases where hours are available for planning, hence it is not well-suited for those cases where the robot activity has to change frequently. By replacing the underlying dynamic programming engine with a different methodology, such as randomized algorithms, the planning time could be controlled to be upper-bounded, thus returning the most efficient solution that can be achieved in the time available for reconfiguring the production. Other applications of interest include optimal ground control of space robotic assets and performance benchmarking of online planning algorithms.

This work studies how force measurement/estimation biases affect the force-based cooperative manipulation of a beam-like load suspended with cables by two aerial robots. Indeed, force biases are especially relevant in a force-based manipulation scenario in which direct communication is not relied upon. First, we compute the equilibrium configurations of the system. Then, we show that inducing an internal force in the load augments the robustness of the load attitude error and its sensitivity to force-bias variations. Eventually, we propose a method for zeroing the load position error. The results are validated through numerical simulations and experiments.

In this work, we exploit the recent notion of closed-loop state sensitivity to critically compare three typical controllers for a quadrotor UAV with the goal of evaluating the impact of controller choice, gain tuning and shape of the reference trajectory in minimizing the sensitivity of the closed-loop system against uncertainties in the model parameters. To this end, we propose a novel optimization problem that takes into account both the shape of the reference trajectory and the controller gains. We then run a large statistical campaign for comparing the performance of the three controllers which provides some interesting insight for the goal of increasing closed-loop robustness against parametric uncertainties.

This article studies how parametric uncertainties affect the cooperative manipulation of a cable-suspended beam-shaped load by means of two aerial robots not explicitly communicating with each other. In particular, this article sheds light on the impact of the uncertain knowledge of the model parameters available to an established communicationless force-based controller. First, we find the closed-loop equilibrium configurations in the presence of the aforementioned uncertainties, and then, we study their stability. Hence, we show the fundamental role played in the robustness of the load attitude control by the internal force induced in the manipulated object by nonvertical cables. Furthermore, we formally study the sensitivity of the attitude error to such parametric variations, and we provide a method to act on the load position error in the presence of uncertainties. Eventually, we validate the results through an extensive set of numerical tests in a realistic simulation environment, including underactuated aerial vehicles and sagging-prone cables, and through hardware experiments.

In this paper we introduce a comprehensive framework to control an aerial manipulator, i.e., an aerial vehicle with a robotic arm, in physical interaction with a human operator or co-worker. The framework uses an admittance control paradigm in order to attain human ergonomy and safety; an interaction supervisor to automatically shape the compliance based on the interaction regions defined around the human co-worker; a projected gradient redundancy resolution scheme to exploit the multiple degrees of freedom of the aerial robot to accommodate for possible additional secondary tasks; and a quadratic programming optimization-based inner loop to cope with real world input saturation and increase the safety level of the human co-worker. The control framework is demonstrated and validated through numerical simulations with a human-in-the loop.

In this article, we consider the problem of delivering an object to a human coworker by means of an aerial robot (AR). To this aim, we present an ergonomics-aware Nonlinear Model Predictive Control (NMPC) designed to autonomously perform the handover. The method is general enough to be applied to any multi-rotor aerial vehicle (MRAV) with a minimal adaptation of the robot model. The formulation of the optimal control problem steers the AR toward a handover location by optimizing the human coworker ergonomics, which includes the predicted arm joint torques of the human. The motion task is expressed in a frame relative to the human, whose motion model is included in the equations of the NMPC. This allows the controller to promptly adapt to the human movements by predicting her future poses over the horizon. The control framework also accounts for the problem of maintaining visibility on the human coworker, while respecting both the actuation and state limits of the robot. Additionally, a safety barrier is embedded in the controller to avoid any risk of collision with the human partner. Realistic simulations are performed to validate the feasibility of the approach and the source code of the implementation is released open-source.

This work introduces a Nonlinear Model Predictive Control (N-MPC) for camera-equipped Unmanned Aerial Vehicles (UAVs), which controls at the motor level the UAV motion to ensure the quality of vision-based state estimation while performing other tasks. The controller ensures visibility over a sufficient amount of features, while optimizing their coverage, based on an assessment of the estimation quality. The controller works for the very broad class of generic multirotor UAVs, including platforms with any number of propellers, which can be both collinear, as in the quadrotor, and fixedly-tilted. The low-level inputs are computed in real-time and realistically constrained, in terms of maximum motor torque. This allows the platform to exploit its full actuation capabilities to maintain the visibility over the set of points of interest. Our implementation is tested in Gazebo simulations and in mocap-free real experiments, and features a visual-inertial state estimation based on Kalman filter. The software is provided open-source.

This article analyzes the evolution and current trends in aerial robotic manipulation, comprising helicopters, conventional underactuated multirotors, and multidirectional thrust platforms equipped with a wide variety of robotic manipulators capable of physically interacting with the environment. It also covers cooperative aerial manipulation and interconnected actuated multibody designs. The review is completed with developments in teleoperation, perception, and planning. Finally, a new generation of aerial robotic manipulators is presented with our vision of the future.

This work addresses the interaction control problem of a fully actuated aerial vehicle considered as a flying end-effector. We tackle the problem using geometrically consistent variable-stiffness impedance control for safe wrench regulation using the concept of energy tanks, where both the modeling and the control are carried out in the port Hamiltonian framework. We exploit previous well-known results in the literature of ground manipulators and extend them to be applied for novel and challenging aerial physical interaction with a focus on quasi-static applications. The energy-awareness of the presented control method guarantees the stability of the aerial robot in both free-flight and in-contact scenarios together with a level of safety in the case of contact-loss with the unknown environment. Furthermore, by utilizing bond graphs we demonstrate how the closed-loop passivity can be graphically conducted. The validity of our proposed approach is shown via several experiments. We also provide several insights on how the proposed framework could be extended to a generic dynamic aerial physical interaction.

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.

In this paper, we present a Perception-constrained Nonlinear Model Predictive Control (NMPC) framework for the real-time control of multi-rotor aerial vehicles. Our formulation considers both constraints from a perceptive sensor and realistic actuator limitations that are the rotor minimum and maximum speeds and accelerations. The formulation is meant to be generic and considers a large range of multi-rotor platforms (such as underactuated quadrotors or tilted-propellers hexarotors) since it does not rely on differential flatness for the dynamical equations, and a broad range of sensors, such as cameras, lidars, etc.... The perceptive constraints are expressed to maintain visibility of a feature point in the sensor's field of view, while performing a reference maneuver. We demonstrate both in simulation and real experiments that our framework is able to exploit the full capabilities of the multi-rotor, to achieve the motion under the aforementioned constraints, and control in real-time the platform at a motor-torque level, avoiding the use of an intermediate unconstrained trajectory tracker.

High risk of a collision between rotor blades and the obstacles in a complex environment imposes restrictions on the aerial manipulators. To solve this issue, a novel system cable-Suspended Aerial Manipulator (SAM) is presented in this paper. Instead of attaching a robotic manipulator directly to an aerial carrier, it is mounted on an active platform which is suspended on the carrier by means of a cable. As a result, higher safety can be achieved because the aerial carrier can keep a distance from the obstacles. For self-stabilization, the SAM is equipped with two actuation systems: winches and propulsion units. This paper presents an overview of the SAM including the concept behind, hardware realization, control strategy, and the first experimental results.

In this paper we propose a novel general method to let a dynamical system fulfil at best a control task when the nominal parameters are not perfectly known. The approach is based on the introduction of the novel concept of closed-loop sensitivity, a quantity that relates parameter variations to deviations of the closed-loop trajectory of the system/controller pair. This new definition takes into account the dependency of the control inputs from the system states and nominal parameters as well as from the controller dynamics. The reference trajectory to be tracked is taken as optimization variable, and the dynamics of both the sensitivity and of its gradient are computed analytically along the system trajectories. We then show how this computation can be effectively exploited for solving trajectory optimization problems aimed at generating a reference trajectory that minimizes a norm of the closed-loop sensitivity. The theoretical results are validated via an extensive campaign of Monte Carlo simulations for two relevant robotic systems: a unicycle and a quadrotor UAV.

This paper presents the OTHex platform for aerial manipulation developed at LAAS-CNRS. The OTHex is probably the first multi-directional thrust platform designed to act as Flying Assistant which can aid human operators and/or Ground Manipulators to move long bars for assembly and maintenance tasks. The work emphasis is on task-driven custom design and experimental validations. The proposed control framework is built around a low-level geometric controller, and includes an external wrench estimator, an admittance filter, and a trajectory generator. This tool gives the system the necessary compliance to resist external force disturbances arising from contact with the surrounding environment or to parameter uncertainties in the load. A set of experiments validates the real-world applicability and robustness of the overall system.

In this paper, we consider a non-conventional hexarotor whose propellers can be simultaneously tilted about two orthogonal axes: in this way, its underactuation degree can be easily adapted to the task at hand. For a given tilt profile, the minimum-energy trajectory between two prescribed boundary states is explicitly determined by solving an optimal control problem with respect to the angular accelerations of the six brushless motors. We also perform, for the first time, a systematic study of the singularities of the control allocation matrix of the hexarotor, showing the presence of subtle singular configurations that should be carefully avoided in the design phase. Numerical experiments conducted with the FAST-Hex platform illustrate the theory and delineate the pros and cons of dual-tilting paradigm in terms of maneuverability and energy efficiency.

The MAGMaS (Multiple Aerial-Ground Manipulator System) was proposed in [1] as a heterogeneous system composed of multiple ground (mobile) manipulators and aerial robots to collaboratively manipulate a long/large-sized object and demonstrated therein for rigid load manipulation. Here, we extend this result of [1] to the case of load manipulation with flexibility, which is crucial for long/slender object manipulation, yet, not considered in [1]. We first provide a rigorous modeling of the load flexibility and its effects on the MAGMaS dynamics. We then propose a novel collaborative control framework for flexible load-tip pose tracking, where the ground manipulator provides slower nominal pose tracking with overall load weight holding, whereas the aerial robot allows for faster vibration suppression with some load weight sharing. We also discuss the issue of controllability stemming from that the aerial robot provides less number of actuation than the modes of the load flexibility; and elucidate some peculiar conditions for this vibration suppression controllability. Simulations are also performed to demonstrate the effectiveness of the proposed theory.

A class of abstract aerial robotic systems is introduced, the laterally bounded force vehicles. in which most of the control authority is expressed along a principal thrust direction, while along the lateral directions a t smaller and possibly null) force may be exploited to achieve full-pose tracking. This class approximates platforms endowed with noncollinear rotors that can modify the orientation of the total thrust in a body frame. If made possible by the force constraints, the proposed SE(3)-based control strategy achieves the independent tracking of position-plus-orientation trajectories. The method, which is proven using a Lyapunov technique, deals seamlessly with both underactuated and fully actuated platforms, and guarantees at least the position tracking in the case of an unfeasible full-pose reference trajectory. Several experimental tests are presented that dearly show the approach practicability and the sharp improvement with respect to state of the art.

In this paper, we shed light on two fundamental actuation capabilities of multirotors. The first is the degree of coupling between the total force and total moment generated by the propellers. The second is the ability to robustly fly completely still in place after the loss of one or more propellers, in the case of mono-directional propellers. These are formalized through the definition of some algebraic conditions on the control allocation matrices. The theory is valid for any multirotor, with arbitrary number, position, and orientation of the propellers. As a show case for the general theory, we demonstrate that standard star-shaped hexarotors with collinear propellers are not able to robustly fly completely still at a constant spot using only five of their six propellers. To deeply understand this counterintuitive result, it is enough to apply our theory, which clarifies the role of the tilt angles and locations of the propellers. The theory is also able to explain why, on the contrary, both the tilted star-shaped and the Y-shaped hexarotors can fly with only five out of six propellers. The analysis is validated with both simulations and extensive experimental results showing recovery control after rotor losses.

This paper introduces the adaptive bias and adaptive gain (ABAG) algorithm for closed-loop electronic speed control (ESC) of the brushless direct current (BLDC) motors typically used to spin the propellers in multi-rotor aerial robots. The ABAG algorithm is adaptive and robust in the sense that it does not require the knowledge of any mechanical/electrical parameter of the motor/propeller group and that neither a pre-calibration nor the knowledge of the feedforward/nominal input is needed. The ABAG algorithm is amenable to an extremely low complexity implementation. We experimentally prove that it can run in 27.5 μs on a 8 MHz microcontroller with no floating point unit and limited arithmetic capabilities allowing only 8-bit additions, subtractions and multiplications. Besides the controller implementation we present a self-contained open source software architecture that handles the entire speed control process, including clock synchronization, and over-current and blockage safeties. The excellent performance and robustness of ABAG are shown by experimental tests and aerial physical interaction experiments.

Standard hexarotors are often mistakenly considered `by definition' fail-safe multi-rotor platforms because of the two additional propellers when compared to quadrotors. However this is not true, in fact, a standard hexarotor cannot statically hover with `only' five propellers. In this paper we provide a set of new general algebraic conditions to ensure static hover for any multi-rotor platform with any number of generically oriented rotors. These are elegantly formulated as the full-rankness of the control moment input matrix, and the non-orthogonality between its null-space and the row space of the control force input matrix. Input saturations and safety margins are also taken into account with an additional condition on the null-space of control moment input matrix. A deep analysis on the hoverability properties is then carried out focusing on the propeller loss in a hexarotor platform. Leveraging our general results we explain why a standard hexarotor is not robust and how it can be made robust thanks to a particular tilt of the rotors. We finally propose a novel cascaded controller based on a preferential direction in the null-space of the control moment input matrix for the large class of statically hoverable multi-rotors, which goes far beyond standard platforms, and we apply this controller to the case of failed tilted hexarotor.

This paper presents the design, control, and experimental validation of a novel fully-actuated aerial robot for physically interactive tasks, named Tilt-Hex. We show how the Tilt-Hex, a tilted-propeller hexarotor is able to control the full pose (position and orientation independently) using a geometric control, and to exert a full-wrench (force and torque independently) with a rigidly attached end-effector using an admittance control paradigm. An outer loop control governs the desired admittance behavior and an inner loop based on geometric control ensures pose tracking. The interaction forces are estimated by a momentum based observer. Control and observation are made possible by a precise control and measurement of the speed of each propeller. An extensive experimental campaign shows that the Tilt-Hex is able to outperform the classical underactuated multi-rotors in terms of stability, accuracy and dexterity and represent one of the best choice at date for tasks requiring aerial physical interaction.

In this paper we lay the foundation of the first heterogeneous multi-robot system of the Multiple Aerial-Ground Manipulator System (MAGMaS) type. A MAGMaS consists of a ground manipulator and a team of aerial robots equipped with a simple gripper manipulator the same object. The idea is to benefit from the advantages of both kinds of platforms, i.e., physical strength versus large workspace. The dynamic model of such robotic systems is derived, and its characteristic structure exhibited. Based on the dynamical structure of the system a nonlinear control scheme, augmented with a disturbance observer is proposed to perform trajectory tracking tasks in presence of model inaccuracies and external disturbances. The system redundancy is exploited by solving an optimal force/torque allocation problem that takes into account the heterogeneous system constraints and maximizes the force manipulability ellipsoid. Simulation results validated the proposed control scheme for this novel heterogeneous robotic system. We finally present a prototypical mechanical design and preliminary experimental evaluation of a MAGMaS composed by a kuka LWR4 and quadrotor based aerial robot.

We present a control methodology for underactuated aerial manipulators that is both easy to implement on real systems and able to achieve highly dynamic behaviors. The method is composed by two parts: i) a nominal input/state trajectory generator that takes into account the full-body dynamics of the system exploiting its differential flatness property; ii) a decentralized feedback controller acting on the actuated degrees of freedom that confers the needed robustness to the closed-loop system. We demonstrate that the proposed controller is able to precisely track dynamic trajectories when implemented on a standard hardware. Comparative experiments clearly show the benefit of using the nominal input/state generator.

In this paper, we consider the problem of controlling an aerial robot connected to the ground by a passive cable or a passive rigid link. We provide a thorough characterization of this nonlinear dynamical robotic system in terms of fundamental properties such as differential flatness, controllability, and observability. We prove that the robotic system is differentially flat with respect to two output pairs: elevation of the link and attitude of the vehicle; elevation of the link and longitudinal link force (e.g., cable tension, or bar compression). We show the design of an almost globally convergent nonlinear observer of the full state that resorts only to an onboard accelerometer and a gyroscope. We also design two almost globally convergent nonlinear controllers to track any sufficiently smooth time-varying trajectory of the two output pairs. Finally, we numerically test the robustness of the proposed method in several far-from-nominal conditions: nonlinear cross-coupling effects, parameter deviations, measurements noise, and nonideal actuators.

In this paper we consider the cooperative control of the manipulation of a load on a plane by a team of mobile robots. We propose two different novel solutions. The first is a controller which ensures exact tracking of the load twist. This controller is partially decentralized since, locally, it does not rely on the state of all the robots but needs only to know the system parameters and load twist. Then we propose a fully decentralized controller that differs from the first one for the use of i) a decentralized estimation of the parameters and twist of the load based only on local measurements of the velocity of the contact points and ii) a discontinuous robustification term in the control law. The second controller ensures a practical stabilization of the twist in presence of estimation errors. The theoretical results are finally corroborated with a simulation campaign evaluating different manipulation settings.

In this paper we present a maneuver regulation scheme for Vertical Take-Off and Landing (VTOL) micro aerial vehicles (MAV). Differently from standard trajectory tracking, maneuver regulation has an intrinsic robustness due to the fact that the vehicle is not required to chase a virtual target, but just to stay on a (properly designed) desired path with a given velocity profile. In this paper we show how a robust maneuver regulation controller can be easily designed by converting an existing tracking scheme. The resulting maneuvering controller has three main appealing features, namely it: (i) inherits the robustness properties of the tracking controller, (ii) gains the appealing features of maneuver regulation, and (iii) does not need any additional tuning with respect to the tracking controller. We prove the correctness of the proposed scheme and show its effectiveness in experiments on a nano-quadrotor. In particular, we show on a nontrivial maneuver how external disturbances acting on the quadrotor cause instabilities in the standard tracking, while marginally affect the maneuver regulation scheme.

In this paper, we propose a bilateral tele-operation scheme for cooperative aerial manipulation in which a human operator drives a team of Vertical Take-Off and Landing (VTOL) aerial vehicles, that grasped an object beforehand, and receives a force feedback depending on the states of the system. For application scenarios in which dexterous manipulation by each robot is not necessary, we propose using a rigid tool attached to the vehicle through a passive spherical joint, equipped with a simple adhesive mechanism at the tool-tip that can stick to the grasped object. Having more than two robots, we use the extra degrees of freedom to find the optimal force allocation in term of minimum power and forces smoothness. The human operator commands a desired trajectory for the robot team through a haptic interface to a pose controller, and the output of the pose controller along with system constraints, e.g., VTOL limited forces and contact maintenance, defines the feasible set of forces. Then, an on-line optimization allocates forces by minimizing a cost function of forces and their variation. Finally, propeller thrusts are computed by a dedicated attitude and thrust controller in a decentralized fashion. Human/Hardware in the loop simulation study shows efficiency of the proposed scheme, and the importance of haptic feedback to achieve a better performance.

We present FAST-Hex, a novel UAV concept which is able to smoothly change its configuration from underactuated to fully actuated by using only one additional motor that tilts all propellers at the same time. FAST-Hex can adapt to the task at hand by finely tuning its configuration from the efficient (but underactuated) flight (typical of coplanar multi-rotor platforms) to the full-pose-tracking (but less efficient) flight, which is attainable by non-coplanar multi-rotors. We also introduce a novel full-pose geometric controller for generic multi-rotors (not only the FAST-Hex) that outperforms classical inverse dynamics approaches. The controller receives as input any reference pose in ℝ3×SO(3) (3D position + 3D orientation). Exact tracking is achieved if the reference pose is feasible with respect to the propeller spinning rate saturations. In case of unfeasibility a new feasible desired trajectory is generated online giving priority to the positional part. The new controller is tested with the FAST-Hex but can be used for many other multi-rotor platforms: underactuated, slightly fully-actuated and completely fully-actuated.

This paper considers the problem of controlling a formation of quadrotor UAVs equipped with onboard cameras able to measure relative bearings in their local body frames w.r.t. neighboring UAVs. The control goal is twofold: (i) steering the agent group towards a formation defined in terms of desired bearings, and (ii) actuating the group motions in the `null-space' of the current bearing formation. The proposed control strategy relies on an extension of the rigidity theory to the case of directed bearing frameworks in ℝ3×S1. This extension allows to devise a decentralized bearing controller which, unlike most of the present literature, does not need presence of a common reference frame or of reciprocal bearing measurements for the agents. Simulation and experimental results are then presented for illustrating and validating the approach.

In this paper we face the challenging problem of takeoff and landing on sloped surfaces for a VTOL aerial vehicle. We define the general conditions for a safe and robust maneuver and we analyze and compare two classes of methods to fulfill these conditions: free-flight vs. passively-tethered. Focusing on the less studied tethered method, we show its advantages w.r.t. the free-flight method thanks to the possibility of inclined hovering equilibria. We prove that the tether configuration and the inclination of the aerial vehicle w.r.t. the slope are flat outputs of the system and we design a hierarchical nonlinear controller based on this property. We then show how this controller can be used to land and takeoff in a robust way without the need of either a planner or a perfect tracking. The validity and applicability of the method in the real world is shown by experiments with a quadrotor that is able to perform a safe landing and takeoff on a sloped surface.

In this paper we introduce a particularly relevant class of aerial manipulators that we name protocentric. These robots are formed by an underactuated aerial vehicle, a planar-Vertical Take-Off and Landing (PVTOL), equipped with any number of different parallel manipulator arms with the only property that all the first joints are attached at the Center of Mass (CoM) of the PVTOL, while the center of actuation of the PVTOL can be anywhere. We prove that protocentric aerial manipulators (PAMs) are differentially flat systems regardless the number of joints of each arm and their kinematic and dynamic parameters. The set of flat outputs is constituted by the CoM of the PVTOL and the absolute orientation angles of all the links. The relative degree of each output is equal to four. More amazingly, we prove that PAMs are differentially flat even in the case that any number of the joints are elastic, no matter the internal distribution between elastic and rigid joints. The set of flat outputs is the same but in this case the total relative degree grows quadratically with the number of elastic joints. We validate the theory by simulating object grasping and transportation tasks with unknown mass and parameters and using a controller based on dynamic feedback linearization.

We present the dynamic modeling, analysis, and control design of a Planar-Vertical Take-Off and Landing (PVTOL) underactuated aerial vehicle equipped either with a rigid- or an elastic-joint arm. We prove that in both cases the system is exactly linearizable with a dynamic feedback and differentially flat for the same set of outputs (but different controllers). We compare the two cases with extensive and realistic simulations, which show that the rigid-joint case outperforms the elastic-joint case for aerial grasping tasks while the converse holds for link-velocity amplification tasks. We present preliminary experimental results using a actuated joint with variable stiffness (VSA) on a quadrotor platform.

We present a decentralized algorithm for estimating mutual poses (relative positions and orientations) in a group of mobile robots. The algorithm uses relative-bearing measurements, which, for example, can be obtained from onboard cameras, and information about the motion of the robots, such as inertial measurements. It is assumed that all relative-bearing measurements are anonymous; i.e., each specifies a direction along which another robot is located but not its identity. This situation, which is often ignored in the literature, frequently arises in practice and remarkably increases the complexity of the problem. The proposed solution is based on a two-step approach: in the first step, the most likely unscaled relative configurations with identities are computed from anonymous measurements by using geometric arguments, while in the second step, the scale is determined by numeric Bayesian filtering based on the motion model. The solution is first developed for ground robots in SE (2) and then for aerial robots in SE (3). Experiments using Khepera III ground mobile robots and quadrotor aerial robots confirm that the proposed method is effective and robust w.r.t. false positives and negatives of the relative-bearing measuring process.

In this paper, a distributed approach for the estimation of kinematic and inertial parameters of an unknown rigid body is presented. The body is manipulated by a pool of ground mobile manipulators. Each robot retrieves a noisy measurement of its velocity and the contact forces applied to the body. Kinematics and dynamics arguments are used to distributively estimate the relative positions of the contact points. Subsequently, distributed estimation filters and nonlinear observers are used to estimate the body mass, the relative position between its geometric center and its center of mass, and its moment of inertia. The manipulation strategy is functional to the estimation process, and is suitably designed to satisfy nonlinear observability conditions that are necessary for the success of the estimation. Numerical results corroborate our theoretical findings.

In this paper a novel teleoperation framework for aerial robots that physically interact with the environment is presented. This framework allows to teleoperate the robot both in contact-free flight and in physical contact with the environment in order, e.g., to apply desired forces on objects of the environment. The framework is build upon an impedance-like indirect interaction force controller that allows to use standard underactuated aerial robots as force effectors. Haptic feedback from the master side enables the user to feel the contact forces exerted by the robot. An automatic potential field-based slowing-down policy is used by the robot to ensure a smooth transition between the contact-free motion phase and the force interaction phase. The effectiveness of the approach has been shown in extensive human-in-the-loop simulations including remote pressing of buttons on a surface and pushing a cart until it touches a wall.

Mobility of a hexarotor UAV in its standard configuration is limited, since all the propeller force vectors are parallel and they achieve only 4-DoF actuation, similar, e.g., to quadrotors. As a consequence, the hexarotor pose cannot track an arbitrary trajectory while the center of mass is tracking a position trajectory. In this paper, we consider a different hexarotor architecture where propellers are tilted, without the need of any additional hardware. In this way, the hexarotor gains a 6-DoF actuation which allows to independently reach positions and orientations in free space and to be able to exert forces on the environment to resist any wrench for aerial manipulation tasks. After deriving the dynamical model of the proposed hexarotor, we discuss the controllability and the tilt angle optimization to reduce the control effort for the specific task. An exact feedback linearization and decoupling control law is proposed based on the input-output mapping, considering the Jacobian and task acceleration, for non-linear trajectory tracking. The capabilities of our approach are shown by simulation results.

We propose and experimentally validate a new class of models for the total thrust generation in multi-rotor UAVs which is suitable for low- and middle-end platforms. Differently from typical models assuming to instantaneously control the rotor spinning velocity, in the proposed class we consider that the total thrust has its own dynamics and its final value explicitly depends both on the pseudo-setpoint commands given to the motor driver and the measurement of the battery terminal voltage. We compare the different model instances within the class using a principled experimental setup in which the total thrust is precisely measured using a motion capture system as ground truth, instead of relying on a setup based or noise-prone force sensors. We then show that the use of a dynamical model that includes also the battery terminal voltage significantly improves the prediction ability of the model in terms of accuracy. Finally we show how the proposed model can be identified using on-board only acceleration measurements, achieving a surprisingly good accuracy when compared with the ground truth case. We expect that the use of the proposed model will be important both in case of precise flight control and in the case of aerial physical interactive tasks.

This work deals with a comprehensive version of the tethered aerial vehicle problem including all the three possible link cases: cable, strut, and bar. We prove the dynamic feedback linearizability and differential flatness of the system with respect to the elevation of the vehicle and the stress applied to the link. Moreover we prove the observability of the system using only on-board inertial sensors (i.e., only a gyroscope plus an accelerometer). We design a globally convergent nonlinear controller based on the concurrent use of a dynamic feedback linearization control and a state estimator based on a nonlinear state/output transformation and a high gain observer scheme. The controller/observer algorithm is thus able to globally control elevation and stress (both tension and compression) along independent time-varying trajectories only resorting to inertial measurements. The stability of the controlled system is theoretically proven and its behavior is shown by means of extensive dynamical simulations.

In this paper we introduce the design of a light-weight novel flexible-joint arm for light-weight unmanned aerial vehicles (UAVs), which can be used both for safe physical interaction with the environment and it represents also a preliminary step in the direction of performing quick motions for tasks such as hammering or throwing. The actuator consists of an active pulley driven by a rotational servo motor, a passive pulley which is attached to a rigid link, and the elastic connections (springs) between these two pulleys. We identify the physical parameters of the system, and use an optimal control strategy to maximize its velocity by taking advantage of elastic components. The prototype can be extended to a light-weight variable stiffness actuator. The flexible-joint arm is applied on a quadrotor, to be used in aerial physical interaction tasks, which implies that the elastic components can also be used for stable interaction absorbing the interactive disturbances which might damage the flying system and its hardware. The design is validated through several experiments, and future developments are discussed in the paper.

We consider the problem of state-observation and control for a bi-tethered aerial system composed by a physical chain of two underactuated aerial robots, also called UAVs. The controlled outputs are the Cartesian position of the last robot and the internal forces along the links. We aim at a minimal use of sensors in order to retrieve the full state. For this goal we propose an output transformation method whose applicability implies the system observability. When this is the case we prove that it is possible to design a nonlinear state estimator based on the high gain- and Luenberger- observers that is able to retrieve the state from any dynamic condition. We also demonstrate how this estimator can be employed with a nonlinear controller for the Cartesian position and the link stresses while ensuring the stability in closed-loop. We show the validity of the method for sensorial configurations composed only by two accelerometers (no gyros) and just two encoders, or two accelerometers (no gyros) and just two inclinometers. A realistic simulative validation concludes the paper.

The flying hand is a robotic hand consisting of a swarm of UAVs able to grasp an object where each UAV contributes to the grasping task with a single contact point at the tooltip. The swarm of robots is teleoperated by a human hand whose fingertip motions are tracked, e.g., using an RGB-D camera. We solve the kinematic dissimilarity of this unique master-slave system using a multi-layered approach that includes: a hand interpreter that translates the fingertip motion in a desired motion for the object to be manipulated; a mapping algorithm that transforms the desired object motions into a suitable set of virtual points deviating from the planned contact points; a compliant force control for the case of quadrotor UAVs that allows to use them as indirect 3D force effectors. Visual feedback is also used as sensory substitution technique to provide a hint on the internal forces exerted on the object. We validate the approach with several human-in-the-loop simulations including the full physical model of the object, contact points and UAVs.

In this paper the problem of a quadrotor that physically interacts with the surrounding environment through a rigid tool is considered. We present a theoretical design that allows to exert an arbitrary 3D force by using a standard near-hovering controller that was originally developed for contact-free flight control. This is achieved by analytically solving the nonlinear system that relates the quadrotor state, the force exerted by the rigid tool on the environment, and the near-hovering controller action at the equilibrium points, during any generic contact. Stability of the equilibria for the most relevant actions (pushing, releasing, lifting, dropping, and left-right shifting) are proven by means of numerical analysis using the indirect Lyapunov method. An experimental platform, including a suitable tool design, has been developed and used to validate the theory with preliminary experiments.

A new framework for semi-autonomous path planning for mobile robots that extends the classical paradigm of bilateral shared control is presented. The path is represented as a B-spline and the human operator can modify its shape by controlling the motion of a finite number of control points. An autonomous algorithm corrects in real time the human directives in order to facilitate path tracking for the mobile robot and ensures i) collision avoidance, ii) path regularity, and iii) attraction to nearby points of interest. A haptic feedback algorithm processes both human's and autonomous control terms, and their integrals, to provide an information of the mismatch between the path specified by the operator and the one corrected by the autonomous algorithm. The framework is validated with extensive experiments using a quadrotor UAV and a human in the loop with two haptic interfaces.

We present the development of a semi-autonomous quadrotor UAV platform for indoor teleoperation using RGB-D technology as exceroceptive sensor. The platform integrates IMU and Dense Visual Odometry pose estimation in order to stabilize the UAV velocity and track the desired velocity commanded by a remote operator though an haptic interface. While being commanded, the quadrotor autonomously performs a persistent pan-scanning of the surrounding area in order to extend the intrinsically limited field of view. The RGB-D sensor is used also for collision-safe navigation using a probabilistically updated local obstacle map. In the operator visual feedback, pan-scanning movement is real time compensated by an IMU-based adaptive filtering algorithm that lets the operator perform the drive experience in a oscillation-free frame. An additional sensory channel for the operator is provided by the haptic feedback, which is based on the obstacle map and velocity tracking error in order to convey information about the environment and quadrotor state. The effectiveness of the platform is validated by means of experiments performed without the aid of any external positioning system.

In this paper we propose a controller, based on an extension of Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) framework, for shaping the whole physical characteristics of a quadrotor and for obtaining a desired interactive behavior between the robot and the environment. In the control design, we shape the total energy (kinetic and potential) of the undamped original system by first excluding external effects. In this way we can assign a new dynamics to the system. Then we apply damping injection to the new system for achieving a desired damped behavior. Then we show how to connect a high-level control input to such system by taking advantage of the new desired physics. We support the theory with extensive simulations by changing the overall behavior of the UAV for different desired dynamics, and show the advantage of this method for sliding on a surface tasks, such as ceiling painting, cleaning or surface inspection.

We study the problem of tracking mobile targets using a team of aerial robots. Each robot carries a camera to detect targets moving on the ground. The overall goal is to plan for the trajectories of the robots in order to track the most number of targets, and accurately estimate the target locations using the images. The two objectives can conflict since a robot may fly to a higher altitude and potentially cover a larger number of targets at the expense of accuracy. We start by showing that k ≥ 3 robots may not be able to track all n targets while maintaining a constant factor approximation of the optimal quality of tracking at all times. Next, we study the problem of choosing robot trajectories to maximize either the number of targets tracked or the quality of tracking. We formulate this problem as the weighted version of a combinatorial optimization problem known as the Maximum Group Coverage (MGC) problem. A greedy algorithm yields a 1/2 approximation for the weighted MGC problem. Finally, we evaluate the algorithm and the sensing model through simulations and preliminary experiments.

This paper presents a novel bilateral controller that allows to stably teleoperate the degree of connectivity in the mutual interaction between a remote group of mobile robots considered as the slave-side. A distributed leader-follower scheme allows the human operator to command the overall group motion. The group autonomously maintains the connectivity of the interaction graph by using a decentralized gradient descent approach applied to the Fiedler eigenvalue of a properly weighted Laplacian matrix. The degree of connectivity, and then the flexibility, of the interaction graph can be finely tuned by the human operator through an additional bilateral teleoperation channel. Passivity of the overall system is theoretically proven and extensive human/hardware in-the-loop simulations are presented to empirically validate the theoretical analysis.

In this paper, an adaptive trajectory tracking controller for quadrotor MAVs is presented. The controller exploits the common assumption of a faster orientation dynamics w.r.t. the translational one, and is able to asymptotically compensate for parametric uncertainties (e.g., displaced center of mass), as well as external disturbances (e.g., wind). The good performance of the proposed controller is then demonstrated by means of an extensive experimental evaluation performed with a commercially-available quadrotor MAV.

In this paper, we investigate the effect of haptic cueing on a human operator's performance in the field of bilateral teleoperation of multiple mobile robots, particularly multiple unmanned aerial vehicles (UAVs). Two aspects of human performance are deemed important in this area, namely, the maneuverability of mobile robots and the perceptual sensitivity of the remote environment. We introduce metrics that allow us to address these aspects in two psychophysical studies, which are reported here. Three fundamental haptic cue types were evaluated. The Force cue conveys information on the proximity of the commanded trajectory to obstacles in the remote environment. The Velocity cue represents the mismatch between the commanded and actual velocities of the UAVs and can implicitly provide a rich amount of information regarding the actual behavior of the UAVs. Finally, the Velocity+Force cue is a linear combination of the two. Our experimental results show that, while maneuverability is best supported by the Force cue feedback, perceptual sensitivity is best served by the Velocity cue feedback. In addition, we show that large gains in the haptic feedbacks do not always guarantee an enhancement in the teleoperator's performance.

Consider a differential-drive mobile robot equipped with an on-board exteroceptive sensor that can estimate its own motion, e.g., a range-finder. Calibration of this robot involves estimating six parameters: three for the odometry (radii and distance between the wheels) and three for the pose of the sensor with respect to the robot. After analyzing the observability of this problem, this paper describes a method for calibrating all parameters at the same time, without the need for external sensors or devices, using only the measurement of the wheel velocities and the data from the exteroceptive sensor. The method does not require the robot to move along particular trajectories. Simultaneous calibration is formulated as a maximum-likelihood problem and the solution is found in a closed form. Experimental results show that the accuracy of the proposed calibration method is very close to the attainable limit given by the Cramér-Rao bound.

We present a decentralized algorithm for estimating mutual 3-D poses in a group of mobile robots, such as a team of UAVs. Our algorithm uses bearing measurements reconstructed, e.g., by a visual sensor, and inertial measurements coming from the robot IMU. Since identification of a specific robot in a group would require visual tagging and may be cumbersome in practice, we simply assume that the bearing measurements are anonymous. The proposed localization method is a non-trivial extension of our previous algorithm for the 2-D case [1], and exhibits similar performance and robustness. An experimental validation of the algorithm has been performed using quadrotor UAVs.

In this paper, we present a passivity-based decentralized approach for bilaterally teleoperating a group of UAVs composing the slave side of the teleoperation system. In particular, we explicitly consider the presence of time delays, both among the master and slave, and within UAVs composing the group. Our focus is on analyzing suitable (passive) strategies that allow a stable teloperation of the group despite presence of delays, while still ensuring high flexibility to the group topology (e.g., possibility to autonomously split or join during the motion). The performance and soundness of the approach is validated by means of human/hardware-in-the-loop simulations (HHIL).

This work extends the framework of bilateral shared control of mobile robots with the aim of increasing the robot autonomy and decreasing the operator commitment. We consider persistent autonomous behaviors where a cyclic motion must be executed by the robot. The human operator is in charge of modifying online some geometric properties of the desired path. This is then autonomously processed by the robot in order to produce an actual path guaranteeing: i) tracking feasibility, ii) collision avoidance with obstacles, iii) closeness to the desired path set by the human operator, and iv) proximity to some points of interest. A force feedback is implemented to inform the human operator of the global deformation of the path rather than using the classical mismatch between desired and executed motion commands. Physically-based simulations, with human/hardware-in-the-loop and a quadrotor UAV as robotic platform, demonstrate the feasibility of the method.

For a quadrotor aircraft, we study the problem of planning a trajectory that connects two arbitrary states while allowing the UAV to grasp a moving target at some intermediate time. To this end, two classes of canonical grasping maneuvers are defined and characterized. A planning strategy relying on differential flatness is then proposed to concatenate one or more grasping maneuvers by means of spline-based subtrajectories, with the additional objective of minimizing the total transfer time. The proposed planning algorithm is not restricted to pure hovering-to-hovering motions and takes into account practical constraints, such as the finite duration of the grasping phase. The effectiveness of the proposed approach is shown by means of physically-based simulations.

In this paper, a novel decentralized control strategy for bilaterally teleoperating heterogeneous groups of mobile robots from different domains (aerial, ground, marine, and underwater) is proposed. By using a decentralized control architecture, the group of robots, which is treated as the slave side, is made able to navigate in a cluttered environment while avoiding obstacles, interrobot collisions, and following the human motion commands. Simultaneously, the human operator acting on the master side is provided with a suitable force feedback informative of the group response and of the interaction with the surrounding environment. Using passivity-based techniques, we allow the behavior of the group to be as flexible as possible with arbitrary split and join events (e.g., due to interrobot visibility/packet losses or specific task requirements) while guaranteeing the stability of the system. We provide a rigorous analysis of the system stability and steady-state characteristics and validate performance through human/hardware-in-the-loop simulations by considering a heterogeneous fleet of unmanned aerial vehicles (UAVs) and unmanned ground vehicles as a case study. Finally, we also provide an experimental validation with four quadrotor UAVs.

The subject of this paper is the patrolling of an environment with the aid of a team of autonomous agents. We consider both the design of open-loop trajectories with optimal properties and of distributed control laws converging to optimal trajectories. As performance criteria, the refresh time and the latency are considered, i.e., respectively, time gap between any two visits of the same region and the time necessary to inform every agent about an event occurred in the environment. We associate a graph with the environment, and we study separately the case of a chain, tree, and cyclic graph. For the case of chain graph, we first describe a minimum refresh time and latency team trajectory and propose a polynomial time algorithm for its computation. Then, we describe a distributed procedure that steers the robots toward an optimal trajectory. For the case of tree graph, a polynomial time algorithm is developed for the minimum refresh time problem, under the technical assumption of a constant number of robots involved in the patrolling task. Finally, we show that the design of a minimum refresh time trajectory for a cyclic graph is NP-hard, and we develop a constant factor approximation algorithm.

The use of multiple unmanned aerial vehicles (UAVs) is increasingly being incorporated into a wide range of teleoperation applications. To date, relevant research has largely been focused on the development of appropriate control schemes. In this paper, we extend previous research by investigating how control performance could be improved by providing the teleoperator with haptic feedback cues. First, we describe a control scheme that allows a teleoperator to manipulate the flight of multiple UAVs in a remote environment. Next, we present three designs of haptic cue feedback that could increase the teleoperator's environmental awareness of such a remote environment. These cues are based on the UAVs' i) velocity information, ii) proximity to obstacles, and iii) a combination of these two sources of information. Finally, we present an experimental evaluation of these haptic cue designs. Our evaluation is based on the teleoperator's perceptual sensitivity to the physical environment inhabited by the multiple UAVs. We conclude that a teleoperator's perceptual sensitivity is best served by haptic feedback cues that are based on the velocity information of multiple UAVs.

In this paper, a novel distributed control strategy for teleoperating a fleet of Unmanned Aerial Vehicles (UAVs) is proposed. Using passivity based techniques, we allow the behavior of the UAVs to be as flexible as possible with arbitrary split and join decisions while guaranteeing stability of the system. Furthermore, the overall teleoperation system is also made passive and, therefore, characterized by a stable behavior both in free motion and when interacting with unknown passive obstacles. The performance of the system is validated through semi-experiments.

We propose a novel haptic teleoperation control framework for multiple unmanned aerial vehicles (UAVs) over the Internet, consisting of the three control layers: 1) UAV control layer, where each UAV is abstracted by, and is controlled to follow the trajectory of, its own kinematic virtual point (VP); 2) VP control layer, which modulates each VP's motion according to the teleoperation commands and local artificial potentials (for inter-VP/VP-obstacle collision avoidance and inter-VP connectivity preservation); and 3) teleoperation layer, through which a remote human user can command all (or some) of the VPs' velocity while haptically perceiving the state of all (or some) of the UAVs over the Internet. Master passivity/slave-stability and some asymptotic performance measures are proved. Semi-experiment results are presented to validate the theory.

We present a decentralized system for the bilateral teleoperation of groups of UAVs which only relies on relative bearing measurements, i.e., without the need of distance information or global localization. The properties of a 3D bearing-formation are analyzed, and a minimal set of bearings needed for its definition is provided. We also design a novel decentralized formation control almost globally convergent and able to maintain bounded and non-vanishing inter-distances among the agents despite the absence of direct distance measurements. Furthermore, we develop a multi-master/ multi-slave teleoperation setup in order to control the overall behavior of the group and to convey to the human operator suitable force cues, while ensuring stability in presence of delays and packet losses over the master-slave communication channel. The theoretical framework is validated by means of extensive human/hardware in-the-loop simulations using two force-feedback devices and a group of quadrotors.

In this paper, we present an experimental validation of a novel decentralized passivity-based control strategy for teleoperating a group of Unmanned Aerial Vehicles (UAVs): the slave side, consisting of the UAVs, is endowed with large group autonomy by allowing time-varying topology and interrobot/obstacle collision avoidance. The master side, represented by a human operator, controls the group motion and receives suitable force feedback cues informing her/him about the remote slave motion status. Passivity theory is exploited for guaranteeing stability of the slave side and of the overall teleoperation channel. Results of experiments involving the use of 4 quadcopters are reported and discussed, confirming the soundness of the paper theoretical claims.

In this paper, we investigate the maneuverability performance of human teleoperators on multi-robots. First, we propose that maneuverability performance can be assessed by a frequency response function that jointly considers the input force of the operator and the position errors of the multi-robot system that is being maneuvered. Doing so allows us to evaluate maneuverability performance in terms of the human teleoperator's interaction with the controlled system. This allowed us to effectively determine the suitability of different haptic cue algorithms in improving teleoperation maneuverability. Performance metrics based on the human teleoperator's frequency response function indicate that maneuverability performance is best supported by a haptic feedback algorithm which is based on an obstacle avoidance force.

Our aim is to afford a remote person the opportunity to participate virtually in some event by using a surrogate robot to communicate with local participants while moving about freely at the venue. Accordingly, we propose a mutual telexistence surrogate robot system, called TELESAR4, which was designed and constructed by development of the following: an immersive audiovisual system; an omnidirectionally mobile robot with a robot arm and hand; an omnidirectional stereo camera system (VORTEX); a head with a retroreflective screen for embodiment of the remote participant; and a retroreflective projection system for local participants. This paper describes the development of the TELESAR4 system, whose efficacy has been verified through demonstration experiments.

This paper addresses a novel visibility-based pursuit-evasion problem in which a team of searchers with limited range sensors must coordinate to clear any evaders from an unknown planar environment. We present a distributed algorithm built around guaranteeing complete coverage of the frontier between cleared and contaminated areas while expanding the cleared area. Our frontier-based algorithm can guarantee detection of evaders in unknown, multiply-connected planar environments which may be non-polygonal. We also detail a method for storing and updating the global frontier between cleared and contaminated areas without building a global map or requiring global localization, which enables our algorithm to be truly distributed. We demonstrate the functionality of the algorithm through Player/Stage simulations.

This paper formulates and investigates a novel problem called Mutual Localization with Anonymous Position Measures. This is an extension of Mutual Localization with Position Measures, with the additional assumption that the identities of the measured robots are not known. A necessary and sufficient condition for the uniqueness of the solution is presented, which requires O(n2= log n) to be verified and is based on the notion of rotational symmetry in R2. We also derive the relationship between the number of robots and the number of possible solutions, and classify the solutions in a number of equivalence classes which is linear in n. A control law is finally proposed that effectively breaks symmetric formations so as to guarantee unique solvability of the problem is also proposed; its performance is illustrated through simulations.

We address the mutual localization problem for a multi-robot system, under the assumption that each robot is equipped with a sensor that provides a measure of the relative position of nearby robots without their identity. Anonymity generates a combinatorial ambiguity in the inversion of the measure equations, leading to a multiplicity of admissible relative pose hypotheses. To solve the problem, we propose a two-stage localization system based on MultiReg, an innovative algorithm that computes on-line all the possible relative pose hypotheses, whose output is processed by a data associator and a multiple EKF to isolate and refine the best estimates. The performance of the mutual localization system is analyzed through experiments, proving the effectiveness of the method and, in particular, its robustness with respect to false positives (objects that look like robots) and false negatives (robots that are not detected) of the measure process.

We present a cooperative exploration strategy for mobile robots. The method is based on the randomized incremental generation of a collection of data structures called sensor-based random trees, each representing a roadmap of an explored area with an associated safe region. Decentralized cooperation and coordination mechanisms are introduced so as to improve the exploration efficiency and to avoid conflicts. Simulations in various environments are presented to show the performance of the proposed technique.