This paper presents a novel cascade nonlinear observer framework for inertial state estimation. It tackles the problem of intermediate state estimation when external localization is unavailable or in the event of a sensor outage. The proposed observer comprises two nonlinear observers based on a recently developed iteratively preconditioned gradient descent (IPG) algorithm. It takes the inputs via an IMU preintegration model where the first observer is a quaternion-based IPG. The output for the first observer is the input for the second observer, estimating the velocity and, consequently, the position. The proposed observer is validated on a public underwater dataset and a real-world experiment using our robot platform. The estimation is compared with an extended Kalman filter (EKF) and an invariant extended Kalman filter (InEKF). Results demonstrate that our method outperforms these methods regarding better positional accuracy and lower variance.

Many traditional feedback-based beamforming techniques can pose risks in environments where adversaries try to jam or detect the beamforming agents. Several low- or no-feedback approaches to beamforming have been proposed to remedy this. Still, problems such as limited transmitter power budgets and inaccurate Channel State Information (CSI) complicate this problem. To that end, this work investigates the use of constrained optimization techniques to allow a team of distributed Unmanned Ground Vehicles (UGVs) to form a beam in the absence of feedback without accurate knowledge of CSI. Specifically, we tune each transmitting agent's signal amplitude and phase offset to selectively build higher-power beams at allied receivers and lower-power nulls at adversarial receivers. To achieve this, we implement a decentralized version of the Frank-Wolfe algorithm, which does not rely on a central coordinator to solve the optimal signal amplitudes and phase offsets without accurate CSI measurements. Numerical results are also provided to validate our approach.

Autonomous navigation in the underwater environment is challenging due to limited visibility, dynamic changes, and the lack of a cost-efficient, accurate localization system. We introduce UIVNAV, a novel end-to-end underwater navigation solution designed to navigate robots over Objects of Interest (OOI) while avoiding obstacles, all without relying on localization. UIVNAVutilizes imitation learning and draws inspiration from the navigation strategies employed by human divers, who do not rely on localization. UIVNAVconsists of the following phases: (1) generating an intermediate representation (IR) and (2) training the navigation policy based on human-labeled IR. By training the navigation policy on IR instead of raw data, the second phase is domain-invariant — the navigation policy does not need to be retrained if the domain or the OOI changes. We demonstrate this within simulation by deploying the same navigation policy to survey two distinct Objects of Interest (OOIs): oyster and rock reefs. We compared our method with complete coverage and random walk methods, showing that our approach is more efficient in gathering information for OOIs while avoiding obstacles. The results show that UIVNAVchooses to visit the areas with larger area sizes of oysters or rocks with no prior information about the environment or localization. Moreover, a robot using UIVNAVcompared to complete coverage method surveys on average 36% more oysters when traveling the same distances. We also demonstrate the feasibility of real-time deployment of UIVNAVin pool experiments with BlueROV underwater robot for surveying a bed of oyster shells.

In this paper, we study integrated estimation and control of soft robots. A significant challenge in deploying closed loop controllers is reliable proprioception via integrated sensing in soft robots. Despite the considerable advances accomplished in fabrication, modelling, and model-based control of soft robots, integrated sensing and estimation is still in its infancy. To that end, this paper introduces a new method of estimating the degree of curvature of a soft robot using a stretchable sensing skin. The skin is a spray-coated piezoresistive sensing layer on a latex membrane. The mapping from the strain signal to the degree of curvature is estimated by using a recurrent neural network. We investigate uni-directional bending as well as bi-directional bending of a single-segment soft robot. Moreover, an adaptive controller is developed to track the degree of curvature of the soft robot in the presence of dynamic uncertainties. Subsequently, using the integrated soft sensing skin, we experimentally demonstrate successful curvature tracking control of the soft robot.

In order to accomplish cooperative tasks, decentralized systems are required to communicate among each other. Thus, maintaining the connectivity of the communication graph is a fundamental issue. Connectivity maintenance has been extensively studied in the last few years, but generally considering undirected communication graphs. In this paper, we introduce a decentralized control and estimation strategy to maintain the strong connectivity property of directed communication graphs. In particular, we introduce a hierarchical estimation procedure that implements power iteration in a decentralized manner, exploiting an algorithm for balancing strongly connected directed graphs. The output of the estimation system is then utilized for guaranteeing preservation of the strong connectivity property. The control strategy is validated by means of analytical proofs and simulation results.

In this paper, the problem of set-point control for flexible-joint robotic manipulators with input/output time delays is investigated. By utilizing scattering transformation with an input-output passive controller, it is demonstrated that the flexible-joint robotic control system can be stabilized when there are time delays in the communication channels. Although stabilization is achieved, the flexible-joint robot cannot be regulated to the desired configuration when utilizing the scattering variables. Hence, a new control framework without scattering transformation is subsequently studied in this paper to guarantee both stability and position regulation provided that the control gain is appropriately selected based on a bound on the time delays. The proposed control algorithms are validated via numerical examples on a two-link flexible-joint robotic manipulator.

This study introduces a control algorithm that, exploiting a completely decentralized estimation strategy for the algebraic connectivity of the graph, ensures the connectivity maintenance property for multi robot systems, in the presence of a generic (bounded) additional control term. This result is obtained by driving the robots along the negative gradient of an appropriately defined function of the algebraic connectivity. The proposed strategy is then enhanced with the introduction of the concept of critical robots, that is robots for which the loss of a single communication link might cause the disconnection of the communication graph. Limiting the control action to critical robots will be shown to reduce the control effort that is introduced by the proposed connectivity maintenance control law and to mitigate its effect on the additional (desired) control term.

In order to accomplish cooperative tasks, multi-robot systems are required to communicate among each other. Thus, maintaining the connectivity of the communication graph is a fundamental issue. Connectivity maintenance has been extensively studied in the last few years, but generally considering only kinematic agents. In this paper we will introduce a control strategy that, exploiting a decentralized procedure for the estimation of the algebraic connectivity of the graph, ensures the connectivity maintenance for groups of Lagrangian systems. The control strategy is validated by means of analytical proofs and simulation results.

Passivity-based control has emerged as an important paradigm for synchronization of networked robotic systems. Despite the practical utility of task-space algorithms, the previous results focused on joint-space synchronization and were primarily derived for kinematically identical manipulators. Hence, in this paper, the problem of task-space synchronization of (possibly redundant) heterogeneous robotic systems is studied. By exploiting passivity-based synchronization results that are developed previously, an adaptive control algorithm is proposed to guarantee task-space synchronization of networked robotic manipulators in the presence of dynamic uncertainties and time-varying communication delays. To demonstrate the efficacy of the proposed framework, numerical simulations and experiments are conducted with redundant and nonredundant manipulators, respectively.

Input/output delays in a control system can pose significant impediments to the stabilization problem and potentially degrade the performance of the closed-loop system. In this paper, we study the classical set-point control problem for rigid robots with input-output communication delays in the closed-loop system. We demonstrate that if there are transmission delays between the robotic system and the controller, then the use of the scattering variables can stabilize an otherwise unstable system for arbitrary unknown constant delays. It is also demonstrated that the proposed algorithm results in guaranteed set-point tracking. In the case of time-varying delays, scattering variables together with additional gains can be utilized to stabilize the closed-loop system that is composed of the robotic manipulator and the controller. Furthermore, a scattering representation-based design with position feedback is proposed to improve closed-loop performance under time-varying delays. The proposed algorithms are validated via experiments in this paper.

Bilateral teleoperation systems have been extensively utilized for implementing tasks in remote or hazardous environments. However, due to the cognitive limitations of the human operator, efficient teleoperation of complex robotic system operating in cluttered environments has been difficult to achieve. In this paper, we study the control problem of a semi-autonomous teleoperation system, where the redundant slave robot can autonomously satisfy several constraints while tracking the position of the master robot in the task space. Considering heterogeneous master and slave robots, we first develop a control algorithm to ensure task space position and velocity tracking between the master and slave robots in the presence of dynamic uncertainties and communication delays. The redundancy of the slave robot is then utilized for achieving sub-task control, such as singularity avoidance, joint limits, and collision avoidance. The control algorithms for the proposed semi-autonomous teleoperation system are validated using numerical simulations on a non-redundant master and a redundant slave robot.

In this paper we present a decentralized control strategy for the connectivity maintenance for groups of mobile robots performing some desired task. Exploiting a completely decentralized estimation strategy for the algebraic connectivity of the graph, we prove the connectivity maintenance property in the general case, i.e. in presence of a generic (bounded) additional control term. Then, we address two specific decentralized control applications: rendezvous and formation control. We analytically prove that our control strategy ensures the global connectivity maintenance, while preserving the convergence properties of the rendezvous and formation controllers respectively. Simulations and experimental results are presented as well, in order to show the effectiveness of the proposed control law.

Time-varying input/output delays in a control system can significantly degrade the stability and performance of the closed loop system. Recently, passivity based control has emerged as a promising candidate to guarantee delay independent stability of passive systems with delays in the input-output channel. In this paper we study set point control of rigid robots with time-varying sensing/control delays. We first show that the classical PD controller can be modified to regulate the robotic manipulator, provided scattering transformation along with additional gains are used in the communication path. While this results in a stable system, asymptotic regulation cannot be guaranteed. Hence, a (delay dependent) gain margin for a proportional position feedback controller is provided to guarantee stability and asymptotic convergence of the regulation error to the origin. To improve closed loop performance, scattering transformation based design of a damping injection scheme is also discussed. The proposed algorithms are numerically verified on a two-degree-of-freedom manipulator.

This paper addresses the problem of steady-state position and force tracking in bilateral teleoperation. Passivity-based control schemes for bilateral teleoperation provide robust stability against network delays in the feedback loop and velocity tracking, but do not guarantee steady-state position and force tracking in general. Position drift due to data loss and offset of initial conditions is a well-known problem in such systems. In this paper, we introduce a new architecture, which builds upon the traditional passivity-based configuration by using additional position control on both the master and slave robots, to solve the steady-state position and force-tracking problem. Lyapunov stability methods are used to establish the range of the position control gains on the master and slave sides. Experimental results using a single-degree-of-freedom master/slave system are presented, showing the performance of the resulting system

In this paper, we investigate issues in the discrete-time implementation of passivity based control of bilateral teleoperators. The usual scattering formalism which, in continuous time, guarantees passivity for any constant delay, is extended in several important ways to the discrete domain, in particular to the case where communication between the master and slave robots occurs over a packet-switched network. We first show that passivity can be maintained in the face of varying delay and packet loss but that it depends fundamentally on the mechanism used to handle missing packets. Passivity alone is not sufficient to guarantee good performance. Therefore, we also introduce a novel buffering and interpolation scheme which not only preserves passivity but has been shown through simulation and experiments to improve tracking performance and transparency in a single-degree-of-freedom teleoperator system.