Paolo Stegagno

University of Rhode Island | URI · Department of Electrical, Computer, and Biomedical Engineering

This paper proposes an adaptive radial basis function neural network (RBF NN) based scheme for the dynamics learning and tracking control problems of a soft trunk robot. Specifically, a low-order approximate model describing the soft robot's dynamics is first derived with the finite element method and proper orthogonal decomposition technique. Base...

This article proposes the application of a distributed containment control algorithm to a team of mobile robots. This paper builds on the containment controller developed by Yuan et al. (2019) for generic linear multi-agent system and tested in simulation only. In this article, we particularize the controller for the case of multiple mobile robots...

In this paper we present a novel formulation of the multi-target tracking problem in which the target represents a variable shape and size subset of the state space. Due to the nonlinear nature of the system and the inherent multi-target nature of the problem, we provide a solution based on the particle implementation of the PHD filter. Testing in...

This paper proposes a novel fault isolation (FI) scheme for distributed parameter systems modeled by a class of parabolic partial differential equations (PDEs) with nonlinear uncertain dynamics. A key feature of the proposed FI scheme is its capability of dealing with the effects of system uncertainties for accurate FI. Specifically, an approximate...

This letter presents a radial basis function neural network (RBF NN) based methodology to investigate the dynamics modeling and fault detection (FD) problems for soft robots. Finite element method (FEM) is first used to derive a mathematical model to describe the dynamics of a soft trunk robot. An adaptive dynamics modeling approach is then designe...

In this paper we propose a landmark-based map localization system for robotic swarms. The proposed system leverages the capabilities of a distributed landmark identification algorithm developed for robotic swarms presented in [1]. The output of the landmark identification consists of a vector of probabilities that each individual robot is looking a...

In this paper, we present a Probability Hypothesis Density (PHD) filter based relative localization system for robotic swarms. The system is designed to use only local information collected by onboard lidar and camera sensors to identify and track other swarm members within proximity. The multi-sensor setup of the system accounts for the inability...

This paper presents a novel bilateral shared framework for a cooperative aerial transportation and manipulation system composed by a team of micro aerial vehicles with a cable-suspended payload. The human operator is in charge of steering the payload and he/she can also change online the desired shape of the formation of robots. At the same time, a...

This paper addresses the problem of cooperative adaptive containment control for multi-agent systems, which specifies the objective of jointly achieving containment control and accurate adaptive learning/identification of unknown system parameters. We consider a class of linear uncertain multi-agent systems with multiple leaders subject to bounded...

This paper investigates the fault detection (FD) problem of a class of uncertain distributed parameter systems modeled by nonlinear parabolic partial differential equations (PDEs). A novel FD scheme is proposed with a neural network-based adaptive dynamics learning approach. Specifically, based on the Galerkin method, a finite dimensional ordinary...

In this paper, we propose a novel intelligent control scheme for a class of discrete-time nonlinear uncertain systems operating under multiple environments/control situations. First, based on the deterministic learning theory, artificial neural networks (NNs) are employed to accurately learn/identify the uncertain system dynamics under each individ...

A high-gain observer-based cooperative deterministic learning (CDL) control algorithm is proposed in this chapter for a group of identical unicycle-type unmanned ground vehicles (UGVs) to track over desired reference trajectories. For the vehicle states, the positions of the vehicles can be measured, while the velocities are estimated using the hig...

This paper addresses the problem of composite adaptive learning and tracking control for discrete-time nonlinear uncertain systems in general normal form. To deal with the system’s unstructured nonlinear uncertain dynamics, novel adaptive neural network (NN) learning control strategies are proposed by extending methodologies from the continuous-tim...

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 un...

This paper addresses the problem of composite tracking control and adaptive learning for discrete-time nonlinear uncertain systems in a general normal form. This problem specifies a joint objective of stable tracking control and accurate learn-ing/identifying the associated ideal control strategy simultaneously , in which the "ideal control strateg...

This paper proposes a human gait recognition method based on a novel adaptive dynamics learning approach (i.e., determin-istic learning) and the Kinect depth camera. First, a series of real-time human joint position data is captured and collected by using Microsoft Kinect and Robot Operating System (ROS) packages. Second, the collected position dat...

This paper addresses the problem of small fault detection for discrete-time nonlinear uncertain systems. The problem is challenging due to (i) the considered system is subject to unstruc-tured nonlinear uncertain dynamics; and (ii) the faults are considered to be "small" in the sense that system states and control inputs in faulty mode remain close...

This paper deals with the problem of cooperative state estimation of general linear multi-agent systems subject to heterogeneous bounded external disturbances. This problem specifies the objective that each agent estimates its own state by using only relative information from its neighbors. Because of the existence of external disturbances, the pro...

In this paper, we present a novel method for controlling an unmanned ground vehicle (UGV) by using a new machine learning technique, called deterministic learning [1], to learn and recognize four specifically designed body languages, which represent four corresponding moving directions (i.e., left, right, up, and down) of the controlled UGV. The Mi...

This paper addresses the problem of small fault detection from closed-loop control for discrete-time nonlinear uncertain systems. The problem is challenging due to (i) the considered system is subject to unstructured nonlinear uncertain dynamics; (ii) the faults are considered to be "small" in the sense that system states and control inputs in faul...

This paper studies the problem of composite synchronization and learning of multiple coordinated robot manipulators subject to heterogeneous nonlinear uncertain dynamics under the leader-follower framework. A new two-layer distributed adaptive learning control scheme is proposed , which consists of the first-layer distributed cooperative estimator...

In this paper, we address the problem of composite cooperative state estimation and system identification for linear multi-agent systems (MASs) under the leader-follower framework. This problem specifies an objective for each follower agent to estimate its local plant state and identify its plant dynamics simultaneously through interacting and comm...

This paper addresses the learning control problem for a group of robot manipulator systems in the presence of homogeneous nonlinear uncertain dynamics, where all the robots have an identical system structure but the reference signals to be tracked are different. Our control law has two objectives: one is tracking control, and the other is learning/...

In this paper, we present an onboard robust nonlinear control approach for quadrotor Unmanned Aerial Vehicles (UAVs) in the environments with disturbances and obstacles. The complete framework consists of an attitude controller based on the solution of global output regulation problems for SO(3), a backstepping-like position controller, a 6-dimensi...

Interaction between humans and unmanned aerial vehicles is a promising field for future applications. However, current interfacing paradigms either imply the presence of intermediary hardware as monitors, joysticks and haptic devices, or are limited to visual/auditory channels with hand gestures, voice recognition, or interpretation of face poses a...

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...

Equipped with four actuators, quadrotor Unmanned Aerial Vehicles belong to the family of underactuated systems. The lateral motion of such platforms is strongly coupled with their orientation and consequently it is not possible to track an arbitrary 6D trajectory in space. In this paper, we propose a novel quadrotor design in which the tilt angles...

We present a control framework for achieving encirclement of a target moving in 3D using a multi-robot system. Three variations of a basic control strategy are proposed for different versions of the encirclement problem, and their effectiveness is formally established. An extension ensuring maintenance of a safe inter-robot distance is also discuss...

The ability to identify the target of a common action is fundamental for the development of a multi-robot team able to interact with the environment. In most existing systems, the identification is carried on individually, based on either color coding, shape identification or complex vision systems. Those methods usually assume a broad point of vie...

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...

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 b...

In this paper we present the design of a platform for autonomous navigation of a quadrotor UAV based on RGB-D technology. The proposed platform can safely navigate in an unknown environment while self-stabilization is done relying only on its own sensor perception. We developed an estimation system based on the integration of IMU and RGB-D measurem...

We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e. measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algor...

We present a control framework for achieving encirclement of a 3D target using a multi-robot system. Three variations of a basic control strategy are proposed for different versions of the encirclement problem, and their effectiveness is formally established. An extension ensuring maintenance of a safe inter-robot distance is also discussed. The pr...

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 s...

We present a method for reconstructing the relative poses among the components of a multi-UAV system using anonymous (i.e., without identity information) robot-to-robot measurements. We consider two cases: bearing-only and bearing+distance measurements. While bearing can be rather directly extracted from a camera image, visual reconstruction of dis...

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 cu...

This paper addresses the problem of mutual localization in multi-robot systems in presence of anonymous (i.e., without the identity information) bearing-only measure- ments. The solution of this problem is relevant for the design and implementation of any decentralized multi-robot algo- rithm/control. A novel algorithm for probabilistic multiple re...

Recent research on multi-agent systems has produced a plethora of decentralized controllers that implicitly assume various degrees of agent localization. However, many practical arrangements commonly taken to allow and achieve localization imply some form of centralization, from the use of physical tagging to allow the identification of the single...

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 pr...

This paper presents a control scheme for localizing and encircling a target using a multi-robot system. The task is achieved in a distributed way, in that each robot only uses local information gathered by on-board relative-position sensors assumed to be noisy, anisotropic, and unable to detect the identity of the measured object. Communication bet...

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...