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Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle : the ideal prediction strategy that knows exactly what the future reference is going to be. Constant : a prediction strategy that ignores every future change and keeps the current vehicle's linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace. [ABSTRACT FROM AUTHOR]

This paper proposes the use of a novel control method based on interconnection and damping assignment–passivity-based control (IDA-PBC) in order to address the aerial physical interaction (APhI) problem for a quadrotor unmanned aerial vehicle (UAV). The apparent physical properties of the quadrotor are reshaped in order to achieve better APhI performances, while ensuring the stability of the interaction through passivity preservation. The robustness of the IDA-PBC method with respect to sensor noise is also analyzed. The direct measurement of the external wrench, needed to implement the control method, is compared with the use of a nonlinear Lyapunov-based wrench observer and advantages/disadvantages of both methods are discussed. The validity and practicability of the proposed APhI method is evaluated through experiments, where for the first time in the literature, a lightweight all-in-one low-cost force/torque (F/T) sensor is used onboard of a quadrotor. Two main scenarios are shown: a quadrotor responding to external disturbances while hovering (physical human–quadrotor interaction), and the same quadrotor sliding with a rigid tool along an uneven ceiling surface (inspection/painting-like task). [ABSTRACT FROM AUTHOR]

In driving simulation, simulator tilt is used to reproduce sustained linear acceleration. In order to feel realistic, this tilt is performed at a rate below the human tilt rate detection threshold, which is usually assumed constant. However, it is known that many factors affect the threshold, such as visual information, simulator motion in additional directions, or the driver’s active effort required for controlling the vehicle. Here we investigated the effect of these factors on the roll rate detection threshold during simulated curve driving. Ten participants reported whether they detected roll motion in multiple trials during simulated curve driving, while roll rate was varied over trials. Roll rate detection thresholds were measured under four conditions. In the first three conditions, participants were moved passively through a curve with the following: (i) roll only in darkness; (ii) combined roll/sway in darkness; (iii) combined roll/sway and visual information. In the fourth (iv) condition participants actively drove through the curve. The results showed that roll rate thresholds in simulated curve driving increase, that is, sensitivity decreases, when the roll tilt is combined with sway motion. Moreover, an active control task seemed to further increase the detection threshold, that is, impair motion sensitivity, but with large individual differences. We hypothesize that this is related to the level of immersion during the task. [ABSTRACT FROM AUTHOR]

For the control of unmanned aerial vehicles (UAVs) in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit (IMU). First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the (unknown) metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter (EKF) formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions. [ABSTRACT FROM AUTHOR]

This work proposes a fully decentralized strategy for maintaining the formation rigidity of a multi-robot system using only range measurements, while still allowing the graph topology to change freely over time. In this direction, a first contribution of this work is an extension of rigidity theory to weighted frameworks and the rigidity eigenvalue, which when positive ensures the infinitesimal rigidity of the framework. We then propose a distributed algorithm for estimating a common relative position reference frame amongst a team of robots with only range measurements in addition to one agent endowed with the capability of measuring the bearing to two other agents. This first estimation step is embedded into a subsequent distributed algorithm for estimating the rigidity eigenvalue associated with the weighted framework. The estimate of the rigidity eigenvalue is finally used to generate a local control action for each agent that both maintains the rigidity property and enforces additional constraints such as collision avoidance and sensing/communication range limits and occlusions. As an additional feature of our approach, the communication and sensing links among the robots are also left free to change over time while preserving rigidity of the whole framework. The proposed scheme is then experimentally validated with a robotic testbed consisting of six quadrotor unmanned aerial vehicles operating in a cluttered environment. [ABSTRACT FROM PUBLISHER]

The design of decentralized controllers coping with the typical constraints on the inter-robot sensing/communication capabilities represents a promising direction in multi-robot research thanks to the inherent scalability and fault tolerance of these approaches. In these cases, connectivity of the underlying interaction graph plays a fundamental role: it represents a necessary condition for allowing a group or robots to achieve a common task by resorting to only local information. The goal of this paper is to present a novel decentralized strategy able to enforce connectivity maintenance for a group of robots in a flexible way, that is, by granting large freedom to the group internal configuration so as to allow establishment/deletion of interaction links at anytime as long as global connectivity is preserved. A peculiar feature of our approach is that we are able to embed into a unique connectivity preserving action a large number of constraints and requirements for the group: (i) the presence of specific inter-robot sensing/communication models; (ii) group requirements such as formation control; and (iii) individual requirements such as collision avoidance. This is achieved by defining a suitable global potential function of the second smallest eigenvalue ?2 of the graph Laplacian, and by computing, in a decentralized way, a gradient-like controller built on top of this potential. Simulation results obtained with a group of quadrotor unmanned aerial vehicles (UAVs) and unmanned ground vehicles, and experimental results obtained with four quadrotor UAVs, are finally presented to thoroughly illustrate the features of our approach on a concrete case study [ABSTRACT FROM AUTHOR]

In this paper we address the problem of controlling the motion of a group of unmanned aerial vehicles (UAVs) bound to keep a formation defined in terms of only relative angles (i.e. a bearing formation). This problem can naturally arise within the context of several multi-robot applications such as, e.g. exploration, coverage, and surveillance. First, we introduce and thoroughly analyze the concept and properties of bearing formations, and provide a class of minimally linear sets of bearings sufficient to uniquely define such formations. We then propose a bearing-only formation controller requiring only bearing measurements, converging almost globally, and maintaining bounded inter-agent distances despite the lack of direct metric information.The controller still leaves the possibility of imposing group motions tangent to the current bearing formation. These can be either autonomously chosen by the robots because of any additional task (e.g. exploration), or exploited by an assisting human co-operator. For this latter ‘human-in-the-loop’ case, we propose a multi-master/multi-slave bilateral shared control system providing the co-operator with some suitable force cues informative of the UAV performance. The proposed theoretical framework is extensively validated by means of simulations and experiments with quadrotor UAVs equipped with onboard cameras. Practical limitations, e.g. limited field-of-view, are also considered. [ABSTRACT FROM PUBLISHER]