Explore the vast range of titles available.

The number of actuators of an underactuated robot is less than its degree of freedom. In other words, underactuated robots can be designed with fewer actuators than fully actuated ones. Although an underactuated robot is more complex than a fully actuated robot, it has many advantages, such as energy, material, and space saving. Therefore, it has high research value in both control theory and practical applications. Swing-up is a mechanism with two links, which mimics a gymnast performing a horizontal bar movement. Over the past few decades, many sufficiently robust control techniques have been developed for a fully actuated robot but almost none of them can be directly applicable to an underactuated robot system. The reason is that such control techniques require certain assumptions that are valid only for fully actuated robot systems but not for underactuated ones. In this paper, a control system design method for underactuated robots based on operator theory and an isomorphism scheme is first proposed. Bezout identity is designed using isomorphism. The effectiveness of the design method is confirmed by simulation. The simulation results show that the performances, such as robust stability and response time, of an underactuated robot control system are improved.

Soft continuum robots are compliant mechanisms that rely on a deformable structure in order to achieve a desired posture. One of the challenges in designing and controlling this type of robot is to obtain the necessary proprioceptive information without resorting to external sensors, like cameras or 3D positioning devices. This requires a reliable and repeatable sensor that can be embedded in the highly deformable structure, distributed along its length, without imposing a significant change to the overall stiffness. This paper presents design considerations and practical results of estimating the tip position of a soft continuum manipulator module using embedded linear magnetic encoders. Three flexible scales with incremental tracks and a magnetic pole pitch of 2 mm are embedded in the robot structure as passive tendons, and six pairs of Hall effect linear sensors are used to measure the relative displacement between points along the outer surface of the structure. The curvature and tip position are then estimated from these measurements. Results are compared with the ground truth measurement of the tip position provided by a commercial optical tracker system. Average error estimates lower than 2.0 mm, with 8.7 mm peak value, were obtained for a robot module with a motion span of approximately 100 mm.

In various fields, planar 2R underactuated robots have garnered significant attention due to their numerous applications. To guarantee the stable control of these robots, a simple control strategy is presented in this paper, and we utilize the intelligent optimization algorithm to enhance the controller parameters. Initially, a comprehensive dynamic model is developed for the robot with its control properties described. Subsequently, we design a PD controller to control the movement of the planar 2R underactuated robot. The differential evolution algorithm (DEA) is used to optimize the parameters of the PD controller to obtain the best control effect and make each link reach the target state. The findings from the simulation demonstrate the efficacy of the approach, and the designed strategy shows a higher stability and convergence rate, highlighting its important contribution to the field of underactuated robots.

For the planar robot with underactuated constraints, a stable control method is presented on the foundation of motion planning method and intelligent optimization, which includes two stages. (1) Designing the controllers to control the actuated manipulators to given target states. (2) Planning the motion trajectory combined with the underactuated constraints between all links, using the intelligent algorithm to find the adaptable trajectory parameters, and tracking such planned trajectories to control full manipulators to the given states simultaneously. At last, multigroup simulations demonstrate the validity of the proposed method.

In underwater environments, ensuring people’s safety is complicated, with potentially life-threatening outcomes, especially when divers have to work in deeper conditions. To improve the available solutions for working with robots in this kind of environment, we propose the validation of a control strategy for robots when taking objects from the seabed. The control strategy proposed is based on acceleration feedback in the model of the system. Using this model, the reference values for position, velocity and acceleration are estimated, and then the position error signal can be computed. When the desired position is obtained, it is possible to then obtain the position error. The validation was carried out using three different objects: a ball, a bottle, and a plant. The experiment consisted of using this control strategy to take those objects, which the robot carried for a moment to validate the stabilisation control and reference following the control in terms of angle and depth. The robot was operated by a pilot from outside of the pool and was guided using a camera and sonar in a teleoperated way. As an advantage of this control strategy, the model upon which the robot is based is decoupled, allowing control of the robot for each uncoupled plane, this being the main finding of these tests. This demonstrates that the robot can be controlled by a control strategy based on a decoupled model, taking into account the hydrodynamic parameters of the robot.

The Twistcar vehicle is a classic example of a nonholonomic dynamical system. The vehicle model consists of two rigid links connected by an actuated rotary joint and supported by wheeled axles, where nonholonomic constraints are assumed to impose no skidding of the wheels. Recent experimental measurements conducted with a robotic Twistcar prototype have shown disagreements with previous theoretical analyses. In particular, significant skidding has been observed, in addition to discrepancies with respect to theoretical predictions of divergence in oscillations of the vehicle’s speed and orientation, as well as direction reversal depending on the vehicle’s structure. The goal of our research is to resolve this disagreement by generalizing the theoretical analysis. First, we extend previous asymptotic analysis by incorporating the effects of links’ inertia and oscillation amplitude of the input angle on the direction of net motion. Next, we formulate the vehicle’s hybrid dynamics under frictional bounds and skid-state transitions. Using numerical analysis, we obtain optimal values for the vehicle’s mean speed and energetic cost-of-transport as a function of the input frequency. Our results improve the agreement between theory and experiments and suggest directions for further experimental investigation.

Identifying objects during the early phases of robotic grasping in unstructured environments is a crucial step toward successful dexterous robotic manipulation. Underactuated hands are versatile and quickly conform to unknown object surfaces to ensure a firm grasp. The trade-off of using such hands is that extracting information and recognizing objects is challenging due to the uncertainty introduced by the hand’s flexibility and unexpected object movements under manipulation. Combining tactile sensors and machine learning models can provide valuable information about manipulated objects to overcome such drawbacks. The present paper explores tactile object identification under two situations: single grasp, analogous to the haptic glance in humans, and through brief exploratory procedures where a robotic thumb displaces the grasped object to excite the sensors. In both scenarios, a fuzzy controller ensures that data collection occurs under approximately the same conditions in terms of forces and vibrations. Machine learning methods used for the single-grasp and short-exploratory data confirm that the former can improve object recognition.

This paper proposes a distributed control method based on the differential flatness (DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.

The control and stabilization problem of the 4-DOF planar inverted pendulum is nontrivial due to the complex nonlinear dynamics and the underactuation of this dynamical system. In this article, a new nonlinear optimal control method is proposed for solving the problem of control and stabilization of the 4-DOF planar (XY) inverted pendulum. To apply the proposed nonlinear optimal control method, the dynamic model of the planar inverted pendulum undergoes first approximate linearization around a temporary operating point that is updated at each iteration of the control algorithm. The linearization takes place through first-order Taylor series expansion and through the computation of the Jacobian matrices of the pendulum’s state-space description. For the approximately linearized model of the planar inverted pendulum an H-infinity feedback controller is designed. Actually, the H-infinity controller stands for the solution of the optimal control problem for the planar inverted pendulum under uncertainty and external perturbations. For the computation of the feedback gains of the H-infinity controller an algebraic Riccati equation is solved at each time-step of the control method. The stability properties of the control algorithm are proven through Lyapunov analysis. The proposed control method achieves fast and accurate tracking of setpoints under moderate variations of the control inputs.