Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-6· 0 citations· 19 references
Abstract
Dexterous in-hand manipulation is becoming increasingly important as robotic systems evolve toward agile, general-purpose automation. This paper presents a decentralized, grid-based trajectory planning approach for in-hand manipulation that coordinates the eleven degrees of freedom of a fully pneumatically actuated anthropomorphic robotic hand. The planner uses uniform time discretization and continuously differentiable second-order point-to-point trajectories in position and velocity, which allows intuitive manual tuning of coordinated multi-actuator motions. Experimental validation on two in-hand manipulation tasks demonstrates smooth, highly dynamic, and repeatable execution of complex ball rotations, despite the lack of sensing for the soft finger actuators and object pose estimation. The results are achieved by combining feedforward control of the soft finger actuators with feedback control of the rigid palm actuators considering friction compensation. The proposed trajectory planning approach is generalizable and transferable to robots with parallel kinematics and partially observed states.
Soft actuators enable dexterous and compliant interaction, but closed-loop task-space control remains challenging due to strong nonlinearities, distributed deformation, and uncertainty in their dynamics. This paper presents a real-time dynamic-model-based task-space feedback and estimation framework based on a non-minimal coordinate discrete elastic rod model formulated in absolute coordinates with holonomic constraints. The resulting structure preserves distributed mechanics while maintaining computational efficiency through sparse system matrices, enabling real-time control with up to 10 discretized rods. A quasi-static feedforward inverse model is combined with a task-space PI controller and a dynamic observer that fuses measurement residuals as virtual forces, enabling full-state estimation from sparse sensing. The approach is experimentally validated on three planar pneumatic soft actuators with varying geometries. Across five tasks, including drawing the digits 0-9 across the workspace (3-18 mm/s tip speed), tracking periodic motion (up to 37 cm/s), cross-platform generalization, reduced sensing conditions, and real-time user-defined references, our method achieves 1.5-2.3 mm root mean square error (RMSE) for precision motions and 5.5-12.4 mm RMSE at 1-2 Hz. Results demonstrate that structured, non-minimal dynamic models can enable real-time, high-precision, moderate-bandwidth task-space control of planar soft pneumatic actuators in free space.
Nithin S. Kumar, Joshua Gaston, D. C. Rucker et al.· 0 citations
We present a robust contact-aware control framework for aerial writing on an underactuated platform. The framework combines nonlinear model predictive control (NMPC) for accurate end-effector position and normal-force tracking at small reference penetration depths, with consistent performance across controller tunings, with whole-body incremental nonlinear dynamic inversion (INDI) for robustness to frictional and aerodynamic disturbances during contact. The proposed controllers are validated on a quadrotor-based aerial manipulator with a rigid, single-link, one-degree-of-freedom (DoF) arm in simulation and real-world experiments. The aerial writing experiments span vertical and inclined surfaces, multiple reference forces, different friction conditions, and wind disturbances. The results demonstrate that robust simultaneous five-DoF end-effector pose and contact-force tracking is achievable on a standard underactuated quadrotor with a simple, rigid, single-link arm, without requiring a fully actuated platform, a complex arm, or dedicated force/torque sensing.
Dual-arm robots often encounter difficulties when handling easily deformable or structurally complex objects using traditional grasping-based manipulation. In addition, grasping and releasing operations introduce significant time overhead. To address these limitations, this paper proposes a vision-based predictive control framework for dual-arm nonprehensile transportation. The proposed method employs a hybrid end effector design that integrates an elastic tether with a tray, enabling flexible and stable transportation without direct grasping. A predictive control strategy is adopted to optimize dual-arm motion trajectories on the move under kinematic and safety constraints. To further enhance coordination accuracy, a direct visual servoing scheme is incorporated to dynamically regulate the arm velocities, minimizing relative motion between the end effectors and the object. This effectively suppresses oscillations induced by the elastic tether. Both simulation and experimental results demonstrate that the proposed approach ensures convergence to desired states and achieves continuous, stable, and safe object transportation, even in the presence of disturbances.
Chang Liu, Yuan Yang, Panfeng Huang et al.· 2026 IEEE International Conf...· 0 citations
Fully actuated UAVs provide enhanced maneuverability and accurate six-degree-of-freedom (6-DoF) control, making them well-suited for demanding tasks such as aerial manipulation, operation in confined environments, and fault-tolerant missions. However, the resulting over-actuation poses challenges in control allocation and robustness to external disturbances. This paper develops a sliding-mode control strategy integrated with a control-allocation framework to achieve precise maneuvering of fully actuated UAVs. The approach is demonstrated on a quadrotor equipped with dual-axis tilting propellers. The proposed guidance framework leverages the vehicle’s full actuation to enable a decoupled design for translational and rotational dynamics. Under the assumption of symmetric actuation across all rotors, thrust vectors are systematically constructed to meet the prescribed tracking objectives. These vectors are then employed in an inverse control-allocation problem to compute the required thrust magnitudes, along with the associated servo and tilt angles for each rotor. Extensive simulations are presented to validate the performance of the proposed scheme across a variety of dynamic reference trajectories.
Rohit V. Nanavati, Abhinav Sinha, S. R. Kumar· International Workshop on Va...· 0 citations
This thesis investigates advanced modeling and control strategies for robotic manipulators, focusing on the DLR-HIT II robotic hand and the KUKA LBR iiwa. It presents three core contributions that integrate simulation, model-based control, and data-driven methods to improve torque and position control under uncertainties and disturbances. First, a dual-platform simulation framework is developed using MATLAB Simscape Multibody and CoppeliaSim. The system accurately models the DLR-HIT II hand’s kinematics and dynamics, enabling both control validation and realistic interaction with virtual environments. The use of Unified Robot Description Format (URDF)-based modeling sup-ports reusability and modular analysis. Second, a physics-informed neural network (PINN) is proposed for direct torque and position control. This method uses only time and joint position inputs, internally computes derivatives, and generalizes well across various trajectory types. It eliminates the need for separate feedback controllers and shows strong robustness under disturbances, while maintaining low computational cost. Third, a Spike-Aware Hybrid Torque Control (SA-HTC) architecture is enhanced with a feedforward neural network (NN) trained offline. The network learns to improve Computed Torque Control (CTC) for unmodeled effects, friction, and external forces by refining the CTC torque output in real time. Simulation results across diverse trajectories and noise levels demonstrate that the SA-HTC method significantly improves tracking accuracy and robustness compared to classical CTC. To support deployment on position-controlled hardware, a physics-informed torque-to-position interface is introduced and compared with virtual stiffness and admittance wrappers, yielding lower steady-state bias, reduced phase lag, lower tracking error, and robust cross-trajectory generalization. Thus, these contributions advance the integration of learning-based and model-based control strategies in robotics. The results highlight scalable and efficient methods for accurate trajectory tracking and torque control, offering practical potential for robotic manipulation and automation applications.
The autonomous robotic manipulators have become inevitable in the contemporary industrial automation, medical robotics, space exploration, and service robots. But it is an inherent challenge to have accurate, active and strong control of robotic arms under dynamic and uncertain conditions. Conventional control techniques utilizing only forward or inverse kinematics have drawbacks of singularities, local minima, sluggish convergence and lack of computational efficiency. In order to solve these problems, the current paper will cover a new autonomous robotic arm control system (ARCs) by relying on a Hybrid Kinematic Optimization(HKO) approach that combines analytical inverse kinematics, numerical optimization, intelligent constraint management. The suggested framework will integrate classical DenavitHartenberg (D-H) kinematic modeling with the use of the gradient-based and evolutionary optimization to produce the optimal joint trajectories in real-time. There is the introduction of a hybrid cost function that involves position accuracy, orientation error, joint smoothness, and energy efficiency. Collision avoidance and workspace constraints are also factored in the control architecture to be used to ensure safe and reliable operation. The hybrid optimizer is a dynamical algorithm that changes between fast analytical solvers and the global numerical optimizers based on the complexity of the task and the environmental conditions. Experiments involving simulation experiments on a 6-DOF model of industrial robotic arm on different task settings, such as pick-and-place settings, obstacle avoidance, and tracking in a trajectory are carried out. Convergence rate, tracking accuracy, joint torque efficiency and computational load are among the performance metrics considered and compared to the more traditional inverse kinematics and pure optimization-based methods. Findings indicate that the suggested hybrid structure attains a maximum speed of convergence is 35 percent, trajectory error is 28 percent and energy use is 22 percent. The Hybrid Kinematic Optimization framework presented provides a robust, scalable and intelligent framework applicable to solve the needs of next generation autonomous robotic manipulators to work within dynamic environments.
Hyeon Woo-Lee, Ken Seok-Park· International Journal of Int...· 0 citations
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