This paper proposes a modification of the Gaussian mixture probability hypothesis density (GM-PHD) filter to compute online the probability of detection$(P_{D})$and probability of survival$(P_{S})$of targets. This eliminates the need for predetermined and/or constant$P_{D}$and$P_{S}$values, that may degrade the estimation. The proposed filter estimates the$P_{D}$and$P_{S}$values for each individual target based on newly introduced parameters, which are updated during the measurement update process. The effectiveness of the proposed filter was validated through an in-lab experiment using four unmanned ground robots with varying$P_{D}$values and a real-world lidar-based obstacle tracking system implemented on an Automated Surface Vehicle operating in a lake with real-time boat traffic. The results of the experiments demonstrate that the proposed filter outperforms the standard PHD filter with incorrect$P_{D}$and$P_{S}$values. These findings highlight the potential benefits of the proposed filter in improving target tracking performance in complex environments.

This article investigates the problem of small fault detection (sFD) for discrete-time nonlinear systems with uncertain dynamics. The faults are considered to be "small" in the sense that the system trajectories in the faulty mode always remain close to those in the normal mode, and the magnitude of fault can be smaller than that of the system's uncertain dynamics. A novel adaptive dynamics learning-based sFD framework is proposed. Specifically, an adaptive dynamics learning approach using radial basis function neural networks (RBF NNs) is first developed to achieve locally accurate identification of the system uncertain dynamics, where the obtained knowledge can be stored and represented in terms of constant RBF NNs. Based on this, a novel residual system is designed by incorporating a newmechanism of absolute measurement of system dynamics changes induced by small faults. An adaptive threshold is then developed for real-time sFD decision making. Rigorous analysis is performed to derive the detectability condition and the analytical upper bound for sFD time. Simulation studies, including an application to a three-tank benchmark engineering system, are conducted to demonstrate the effectiveness and advantages of the proposed approach.

Indoor operation of unmanned aerial vehicles (UAV s) poses many challenges due to the lack of GPS signal and cramped spaces. The presence of obstacles in an unfamiliar environment requires reliable state estimation and active algorithms to prevent collisions. In this paper, we present a teleoperated quadrotor UAV platform equipped with an onboard miniature computer and a minimal set of sensors for this task. The platform is capable of highly accurate state-estimation, tracking of desired velocity commanded by the user and ensuring collision-free navigation. The robot estimates its linear velocity through a Kalman filter integration of inertial and optical flow (OF) readings with corresponding distance measurements. An RGB-D camera serves the purpose of providing visual feedback to the operator and depth measurements to build a probabilistic, robo-centric obstacle model, allowing the robot to avoid collisions. The platform is thoroughly validated in experiments in an obstacle rich environment.

Physical human-UAV interaction (PHUI) with Unmanned Aerial Vehicles (UAV) has many possible applications, and a few recent works [1], [2] have shown that it is possible to perform PHUI with a standard quadrotor. However, the intrinsic underactuation of quadrotors may hinder the interaction task and also cause safety issues. In this paper, we present an admittance control-based scheme to perform PHUI with a fully actuated UAV. The system also benefits from the robustness provided by our fully actuated UAV implementation of the adaptive super twisting controller (ASTC). While validating our system in simulation, we also show the superior performance of the fully actuated UAV with respect to a standard quadcopter.

In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robot's velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.

In this paper we present a robust quadrotor controller for tracking a reference trajectory in presence of uncertainties and disturbances. A Super Twisting controller is implemented using the recently proposed gain adaptation law [1], [2], which has the advantage of not requiring the knowledge of the upper bound of the lumped uncertainties. The controller design is based on the regular form of the quadrotor dynamics, without separation in two nested control loops for position and attitude. The controller is further extended by a feedforward dynamic inversion control that reduces the effort of the sliding mode controller. The higher order quadrotor dynamic model and proposed controller are validated using a SimMechanics physical simulation with initial error, parameter uncertainties, noisy measurements and external perturbations.

This paper addresses the problem of cooperative aerial transportation of an object using a team of quadrotors. The approach presented to solve this problem accounts for the full dynamics of the system and it is inspired by the literature on reconfigurable cable-driven parallel robots (RCDPR). Using the modelling convention of RCDPR it is derived a direct relation between the motion of the quadrotors and the motion of the payload. This relation makes explicit the available internal motion of the system, which can be used to automatically achieve additional tasks. The proposed method does not require to specify a priory the forces in the cables and uses a tension distribution algorithm to optimally distribute them among the robots. The presented framework is also suitable for online teleoperation. Physical simulations with a human-in-the-loop validate the proposed approach.

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.

This paper presents a nonlinear control approach for quadrotor Micro Aerial Vehicles (MAVs), which combines a backstepping-like regulator based on the solution of a certain class of global output regulation problems for the rigid body equations on SO(3), a robust controller for the system with bounded disturbances, as well as a trajectory generator using a model predictive control method. The proposed algorithm is endowed with strong convergence properties so that it allows the quadrotor MAVs to reach almost all the desired attitudes. The control approach is implemented on a high-payload-capable quadcopter with unstructured dynamics and unknown disturbances. The performance of our algorithm is demonstrated through a series of experimental evaluations and comparisons with another control method on normal and aggressive trajectory tracking tasks.

Identification of landmarks for outdoor navigation is often performed using computationally expensive computer vision methods or via heavy and expensive multi-spectral and range sensors. Both choices are forbidden on Micro Aerial Vehicles (MAV) due to limited payload and computational power. However, an appropriate choice of the hardware sensor equipment allows the employment of mixed multi-spectral analysis and computer vision techniques to identify natural landmarks. In this work, we propose a low-cost low-weight camera array with appropriate optical filters to be exploited both as stereo camera and multi-spectral sensor. Through stereo vision and the Normalized Difference Vegetation Index (NDVI), we are able to classify the observed materials in the scene among several different classes, identify vegetation and water bodies and provide measurements of their relative bearing and distance from the robot. A handheld prototype of this camera array is tested in outdoor environment.

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.

We propose a cooperative control scheme for a heterogeneous multi-robot system, consisting of an Unmanned Aerial Vehicle (UAV) equipped with a camera and multiple identical Unmanned Ground Vehicles (UGVs). Our control scheme takes advantage of the different capabilities of the robots. Since the system is highly redundant, the execution of multiple different tasks is possible. The primary task is aimed at keeping the UGVs well inside the camera field of view, so as to allow our localization system to reconstruct the identity and relative pose of each UGV with respect to the UAV. Additional tasks include formation control, navigation and obstacle avoidance. We thoroughly discuss the feasibility of each task, proving convergence when possible. Simulation results are presented to validate the proposed method.

We develop a localization method for a single-UAV/multi-UGV heterogeneous system of robots. Considering the natural supervisory role of the UAV and the challenging (but realistic) assumption that the UAV-to-UGV measurements do not include the identities of the UGVs, we have adopted the PHD filter as a multi-target tracking technique. However, the standard version of this filter does not take into account odometric information coming from the targets, nor does it solve the problem of estimating their identities. Hence, we design ID-PHD, a modification of the PHD filter that is able to reconstruct the identities of the targets by incorporating odometric data. The proposed localization method has been successfully validated through experiments. Some preliminary results of a localization-based control scheme for the multi-robot system are also presented.

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.

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 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.