arXiv subjects

Farhad Aghili

Publications and source records attributed to Farhad Aghili.

At least 19 recordsLinked to original sources

arXiv2026

Hadamard--Tetrahedral Symmetric Spacetime and Clifford-Like Factorizations of Dirac and Maxwell Operators

A symmetric representation of Minkowski spacetime is constructed by combining a normalized fourth-order Hadamard transformation with an imaginary temporal coordinate. The resulting complex coordinates $q^a$ distribute time and space equally among four components and transform the Minkowski metric into a Euclidean-form complex bilinear metric. The four Hadamard spatial sign vectors form a regular tetrahedron and appear simultaneously in the transformed Dirac and Maxwell operators. The Dirac matrices satisfy a Euclidean-form Clifford algebra and contain tetrahedral combinations of Pauli matrices, whereas the Riemann--Silberstein Maxwell matrices realize the corresponding spin-1 structure. A gauge-invariant QED Lagrangian is formulated in the same coordinates. In Fourier space, the Dirac and Maxwell systems are governed by the common invariant $κ_aκ^a$, while the complete Maxwell operator admits an exact rectangular Clifford-like factorization. The formulation is further extended to the weak-field Einstein--Dirac--Maxwell system. In harmonic gauge, the linearized gravitational field is governed by the same scalar wave operator $\Box_q$, while gravitational corrections to the Dirac and Maxwell equations enter through the perturbed tetrad, spin connection, and curvature. This common principal-operator structure suggests shared spectral kernels and scalar-wave preconditioning for coupled field solvers.

math-ph↗

arXiv2026

Active Stiffness Control of a Supportive Continuum Robot

Supportive continuum robots (SCRs) enhance the load-bearing capability of an operative continuum robot by mechanically coupling it with a supportive arm. However, their passive stiffness is determined by the mechanical configuration and cannot be adjusted online for varying payloads or interaction forces. Active stiffness control is therefore needed to regulate the load response and maintain positioning accuracy. Meanwhile, the closed-chain structure introduces kinematic constraints that complicate task-space regulation and stiffness control. This paper presents an active task-space stiffness control framework for a tendon-driven SCR. An existing geometric variable strain model describes the closed-chain dynamics, which are projected onto the constraint-consistent motion subspace. A projected sliding mode controller regulates the operative arm tip while preserving the constraints, and closed-loop stability is established through Lyapunov analysis. After position regulation, active apparent stiffness is introduced through a virtual Cartesian spring based on position-error feedback to shape the force--displacement response. The framework is evaluated in simulation and experimentally validated under prescribed external loads and different desired configurations. Results show that increasing the commanded stiffness gain reduces load-induced tip deflection and increases apparent directional stiffness, thereby improving load resistance and positioning robustness under external loading.

cs.RO↗

arXiv2026

AI-Enabled Image-Based Hybrid Vision/Force Control of Tendon-Driven Aerial Continuum Manipulators

This paper presents an AI-enabled cascaded hybrid vision/force control framework for tendon-driven aerial continuum manipulators based on constant-strain modeling in $SE(3)$ as a coupled system. The proposed controller is designed to enable autonomous, physical interaction with a static environment while stabilizing the image feature error. The developed strategy combines the cascaded fast fixed-time sliding mode control and a radial basis function neural network to cope with the uncertainties in the image acquired by the eye-in-hand monocular camera and the measurements from the force sensing apparatus. This ensures rapid, online learning of the vision- and force-related uncertainties without requiring offline training. Furthermore, the features are extracted via a state-of-the-art graph neural network architecture employed by a visual servoing framework using line features, rather than relying on heuristic geometric line extractors, to concurrently contribute to tracking the desired normal interaction force during contact and regulating the image feature error. A comparative study benchmarks the proposed controller against established rigid-arm aerial manipulation methods, evaluating robustness across diverse scenarios and feature extraction strategies. The simulation and experimental results showcase the effectiveness of the proposed methodology under various initial conditions and demonstrate robust performance in executing manipulation tasks.

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arXiv2026

Real-Time Projected Adaptive Control for Closed-Chain Co-Manipulative Continuum Robots

In co-manipulative continuum robots (CCRs), multiple continuum arms cooperate by grasping a common flexible object, forming a closed-chain deformable mechanical system. The closed-chain coupling induces strong dynamic interactions and internal reaction forces. Moreover, in practical tasks, the flexible object's physical parameters are often unknown and vary between operations, rendering nominal model-based controllers inadequate. This paper presents a projected adaptive control framework for CCRs formulated at the dynamic level. The coupled dynamics are expressed using the Geometric Variable Strain (GVS) representation, yielding a finite-dimensional model that accurately represents the system, preserves the linear-in-parameters structure required for adaptive control, and is suitable for real-time implementation. Closed-chain interactions are enforced through Pfaffian velocity constraints, and an orthogonal projection is used to express the dynamics in the constraint-consistent motion subspace. Based on the projected dynamics, an adaptive control law is developed to compensate online for uncertain dynamic parameters of both the continuum robots and the manipulated flexible object. Lyapunov analysis establishes closed-loop stability and convergence of the task-space tracking errors to zero. Simulation and experiments on a tendon-driven CCR platform validate the proposed framework in task-space regulation and trajectory tracking.

cs.RO↗

arXiv2025

Analytic Continuation of Divergent Integrals

In this work, we investigate the improper integral of the monomial $μ(s) = \int_1^{\infty} x^{-s} \,dx $ as a continuous analogue of the infinite series representation of the Riemann $ζ$-function, $ζ(s) = \sum_{n=1}^{\infty} n^{-s}$. Both the monomial integral and the corresponding series converge for $\mathrm{Re}(s) > 1$ and diverge for $s \in \mathbb{C}$ with $\mathrm{Re}(s) \leq 1$. In this paper, we construct an analytic continuation of the divergent monomial integral to the entire complex plane, excluding a simple pole at $s = 1$, mirroring the analytic continuation of the $ζ$-function. By performing term-by-term integration of the monomial over successive integer intervals and leveraging Newton's generalization of the binomial theorem, we express the improper integral as a Dirichlet series. This approach establishes an elegant relationship between the $μ$-function and the $ζ$-function, leading to a functional equation that extends the divergent integral through analytic continuation and that the $μ$-function is holomorphic everywhere except at $s = 1$.

math.NT↗

arXiv2025

Hybrid Visual Servoing of Tendon-driven Continuum Robots

This paper introduces a novel Hybrid Visual Servoing (HVS) approach for controlling tendon-driven continuum robots (TDCRs). The HVS system combines Image-Based Visual Servoing (IBVS) with Deep Learning-Based Visual Servoing (DLBVS) to overcome the limitations of each method and improve overall performance. IBVS offers higher accuracy and faster convergence in feature-rich environments, while DLBVS enhances robustness against disturbances and offers a larger workspace. By enabling smooth transitions between IBVS and DLBVS, the proposed HVS ensures effective control in dynamic, unstructured environments. The effectiveness of this approach is validated through simulations and real-world experiments, demonstrating that HVS achieves reduced iteration time, faster convergence, lower final error, and smoother performance compared to DLBVS alone, while maintaining DLBVS's robustness in challenging conditions such as occlusions, lighting changes, actuator noise, and physical impacts.

cs.RO↗

arXiv2024

Impedance Control for Manipulators Handling Heavy Payloads

Attaching a heavy payload to the wrist force/moment (F/M) sensor of a manipulator can cause conventional impedance controllers to fail in establishing the desired impedance due to the presence of non-contact forces; namely, the inertial and gravitational forces of the payload. This paper presents an impedance control scheme designed to accurately shape the force-response of such a manipulator without requiring acceleration measurements. As a result, neither wrist accelerometers nor dynamic estimators for compensating inertial load forces are necessary. The proposed controller employs an inner-outer loop feedback structure, which not only addresses uncertainties in the robot's dynamics but also enables the specification of a general target impedance model, including nonlinear models. Stability and convergence of the controller are analytically proven, with results showing that the control input remains bounded as long as the desired inertia differs from the payload inertia. Experimental results confirm that the proposed impedance controller effectively shapes the impedance of a manipulator carrying a heavy load according to the desired impedance model.

cs.RO↗

arXiv2024

Seamless Capture and Stabilization of Spinning Satellites By Space Robots with Spinning Base

This paper introduces an innovative guidance and control method for simultaneously capturing and stabilizing a fast-spinning target satellite, such as a spin-stabilized satellite, using a spinning-base servicing satellite equipped with a robotic manipulator, joint locks, and reaction wheels (RWs). The method involves controlling the RWs of the servicing satellite to replicate the spinning motion of the target satellite, while locking the manipulator's joints to achieve spin-matching. This maneuver makes the target stationary with respect to the rotating frame of the servicing satellite located at its center-of-mass (CoM), simplifying the robot capture trajectory planning and eliminating post-capture trajectory planning entirely. In the next phase, the joints are unlocked, and a coordination controller drives the robotic manipulator to capture the target satellite while maintaining zero relative rotation between the servicing and target satellites. The spin stabilization phase begins after completing the capture phase, where the joints are locked to form a single tumbling rigid body consisting of the rigidly connected servicing and target satellites. An optimal controller applies negative control torques to the RWs to dampen out the tumbling motion of the interconnected satellites as quickly as possible, subject to the actuation torque limit of the RWs and the maximum torque exerted by the manipulator's end-effector.

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arXiv2023

Autonomous Robots for Active Removal of Orbital Debris

This paper presents a vision guidance and control method for autonomous robotic capture and stabilization of orbital objects in a time-critical manner. The method takes into account various operational and physical constraints, including ensuring a smooth capture, handling line-of-sight (LOS) obstructions of the target, and staying within the acceleration, force, and torque limits of the robot. Our approach involves the development of an optimal control framework for an eye-to-hand visual servoing method, which integrates two sequential sub-maneuvers: a pre-capturing maneuver and a post-capturing maneuver, aimed at achieving the shortest possible capture time. Integrating both control strategies enables a seamless transition between them, allowing for real-time switching to the appropriate control system. Moreover, both controllers are adaptively tuned through vision feedback to account for the unknown dynamics of the target. The integrated estimation and control architecture also facilitates fault detection and recovery of the visual feedback in situations where the feedback is temporarily obstructed. The experimental results demonstrate the successful execution of pre- and post-capturing operations on a tumbling and drifting target, despite multiple operational constraints.

cs.RO↗

arXiv2023

Coordination Control of Free-Flyer Manipulators

This paper presents a method for guiding a robot manipulator to capture and bring a tumbling satellite to a state of rest. The proposed approach includes developing a coordination control for the combined system of the space robot and the target satellite, where the satellite acts as the manipulator payload. This control ensures that the robot tracks the optimal path while regulating the attitude of the chase vehicle to a desired value. Two optimal trajectories are then designed for the pre- and post-capture phases. In the pre-capturing phase, the manipulator manoeuvres are optimized by minimizing a cost function that includes the time of travel and the weighted norms of the end-effector velocity and acceleration, subject to the constraint that the robot end-effector and a grapple fixture on the satellite arrive at the rendezvous point with the same velocity. In the post-grasping phase, the manipulator dumps the initial velocity of the tumbling satellite in minimum time while ensuring that the magnitude of the torque applied to the satellite remains below a safe value. Overall, this method offers a promising solution for effectively capturing and bringing tumbling satellites to a state of rest.

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arXiv2022

Automated Rendezvous & Docking Using 3D Vision

The robustness and accuracy of a vision system for motion estimation of a tumbling target satellite are enhanced by an adaptive Kalman filter. This allows a vision-guided robot to complete the grasping of the target even if occlusion occurs during the operation. A complete dynamics model, including aspects of orbital mechanics, is incorporated for accurate estimation. Based on the model, an adaptive Kalman filter is developed that estimates not only the system states but also all the model parameters such as the inertia ratio, center-of-mass, and the rotation of the principal axes of the target satellite. An experiment is conducted by using a robotic arm to move a satellite mockup according to orbital mechanics while the satellite pose is measured by a laser camera system. The measurements are sent to the Kalman filter, which, in turn, drives another robotic arm to grasp the target. The results demonstrate successful grasping even if the vision system is blocked for several seconds.

cs.RO↗

arXiv2022

Control and Simulation of Motion of Constrained Multibody Systems Based on Projection Matrix Formulation

This paper presents a unified approach for inverse and direct dynamics of constrained multibody systems that can serve as a basis for analysis, simulation, and control. The main advantage of the formulation of the dynamic is that it does not require the constraint equations to be linearly independent. Thus, a simulation may proceed even in the presence of redundant constraints or singular configurations and a controller does not need to change its structure whenever the mechanical system changes its topology or number of degrees of freedom. A motion control scheme is proposed based on a projected inverse-dynamics scheme which proves to be stable and minimizes the weighted Euclidean norm of the actuation force. The projection-based control scheme is further developed for constrained systems, e.g. parallel manipulators, which have some joints with no actuators (passive joints). This is complemented by the development of constraint force control. A condition on the inertia matrix resulting in a decoupled mechanical system is analytically derived which simplifies the implementation of the force control.

eess.SY↗

arXiv2022

Hybrid Simulator for Space Docking and Robotic Proximity Operations

In this work, we present a hybrid simulator for space docking and robotic proximity operations methodology. This methodology also allows for the emulation of a target robot operating in a complex environment by using an actual robot. The emulation scheme aims to replicate the dynamic behavior of the target robot interacting with the environment, without dealing with a complex calculation of the contact dynamics. This method forms a basis for the task verification of a flexible space robot. The actual emulating robot is structurally rigid, while the target robot can represent any class of robots, e.g., flexible, redundant, or space robots. Although the emulating robot is not dynamically equivalent to the target robot, the dynamical similarity can be achieved by using a control law developed herein. The effect of disturbances and actuator dynamics on the fidelity and the contact stability of the robot emulation is thoroughly analyzed.

cs.RO↗

arXiv2022

Six-DOF Spacecraft Dynamics Simulator For Testing Translation and Attitude Control

This paper presents a method to control a manipulator system grasping a rigid-body payload so that the motion of the combined system in consequence of externally applied forces to be the same as another free-floating rigid-body (with different inertial properties). This allows zero-g emulation of a scaled spacecraft prototype under the test in a 1-g laboratory environment. The controller consisting of motion feedback and force/moment feedback adjusts the motion of the test spacecraft so as to match that of the flight spacecraft, even if the latter has flexible appendages (such as solar panels) and the former is rigid. The stability of the overall system is analytically investigated, and the results show that the system remains stable provided that the inertial properties of two spacecraft are different and that an upperbound on the norm of the inertia ratio of the payload to manipulator is respected. Important practical issues such as calibration and sensitivity analysis to sensor noise and quantization are also presented.

cs.RO↗

arXiv2022

Adaptive Machine Learning for Cooperative Manipulators

The problem of self-tuning control of cooperative manipulators forming a closed kinematic chain in the presence of an inaccurate kinematics model is addressed using adaptive machine learning. The kinematic parameters pertaining to the relative position/orientation uncertainties of the interconnected manipulators are updated online by two cascaded estimators in order to tune a cooperative controller for achieving accurate motion tracking with minimum-norm actuation force. This technique permits accurate calibration of the relative kinematics of the involved manipulators without needing high precision end-point sensing or force measurements, and hence it is economically justified. Investigating the stability of the entire real-time estimator/controller system reveals that the convergence and stability of the adaptive control process can be ensured if i) the direction of the angular velocity vector does not remain constant over time, and ii) the initial kinematic parameter error is upper bounded by a scaler function of some known parameters. The adaptive controller is proved to be singularity-free even though the control law involves inverting the approximation of a matrix computed at the estimated parameters. Experimental results demonstrate the sensitivity of the tracking performance of the conventional inverse dynamic control scheme to kinematic inaccuracies, while the tracking error is significantly reduced by the self-tuning cooperative controller.

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arXiv2022

Adaptive Visual Servo Control for Autonomous Robots

This paper focuses on an adaptive and fault-tolerant vision-guided robotic system that enables to choose the most appropriate control action if partial or complete failure of the vision system in the short term occurs. Moreover, the autonomous robotic system takes physical and operational constraints into account to perform the demands of a specific visual servoing task in a way to minimize a cost function. A hierarchical control architecture is developed based on interwoven integration of a variant of the iterative closest point (ICP) image registration, a constrained noise-adaptive Kalman filter, a fault detection logic and recovery, together with a constrained optimal path planner. The dynamic estimator estimates unknown states and uncertain parameters required for motion prediction while imposing a set of inequality constraints for consistency of the estimation process and adjusting adaptively the Kalman filter parameters in the face of unexpected vision errors. It is followed by the implementation of a fault recovery strategy based on a fault detection logic that monitors the health of the visual feedback using the metric fit error of the image registration. Subsequently, the estimated/predicted pose and parameters are passed to an optimal path planner in order to bring the robot end-effector to the grasping point of a moving target as quickly as possible subject to multiple constraints such as acceleration limit, smooth capture, and line-of-sight angle of the target.

cs.RO↗

arXiv2022

Robust 3D Vision for Autonomous Robots

This paper presents a fault-tolerant 3D vision system for autonomous robotic operation. In particular, pose estimation of space objects is achieved using 3D vision data in an integrated Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by the state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states but also its inertial parameters. This allows the motion of the target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to the increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking. In this experiment, Neptec's Laser Camera System (LCS) is used for real-time scanning of a satellite model attached to a manipulator arm, which is driven by a simulator according to orbital and attitude dynamics. The results showed that robust tracking of the free-floating tumbling satellite can be achieved only if the Kalman filter and ICP are in a closed-loop configuration.

cs.RO↗

arXiv2022

Lidar SLAM for Autonomous Driving Vehicles

This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix, and the observation sensitivity matrix are derived in closed-form making them suitable for real-time implementation. Examining the observability of the 3D SLAM system leads to the conclusion that the system remains observable upon a geometrical condition on the alignment of the landmarks.

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