Mechatronics最新文献

Ultra-wideband (UWB) sensor-based localization systems have been widely used in various applications that require precise positioning. However, most existing localization systems in the literature have limited coverage areas due to the stationary anchors or ground control station (GCS)-based system. To address this limitation, this paper proposes a mobile UWB-based onboard localization system composed of multi-drones with free structural anchors. In this paper, we introduce the detailed hardware and software configuration of the proposed system, based on the robot operating system (ROS). Moreover, we present a modified multilateration method for real-time onboard localization, which improves the localization performance even in the case of coplanar anchors by adaptively adjusting the number of iterations based on the notion of innovation. Finally, the proposed system is verified through outdoor experiments, and the results are compared with existing localization methods.

Multiple diseases and injuries can cause knee joint stiffness. Typically, therapists manually move a patient’s lower limb to assist them in regaining range of motion. There are also devices that can be used to aid in the rehabilitation process; however, the majority of them require lengthy rehabilitation sessions or have a fixed axis of rotation that cannot always be aligned with the knee’s moving center of rotation. This work presents the design, fabrication, and evaluation of a soft, inflatable, wearable device without rigid mechanisms that aims to replicate the behavior of the therapist and address the mentioned deficiencies. Two control strategies, passive and assist-as-needed, are defined for the device’s operation. The objective of the passive strategy is to relocate the patient’s knee to the designated position within the specified time. The assist-as-needed strategy, on the other hand, does not interfere if the patient is able to move ahead of the trajectory that the device is following, and only begins to assist when the patient’s limb stops moving. The device underwent experimental testing, and the outcomes were assessed.

This paper presents an efficient in-field calibration method tailored for low-cost triaxial MEMS gyroscopes often used in healthcare applications. Traditional calibration techniques are challenging to implement in clinical settings due to the unavailability of high-precision equipment. Unlike the auto-calibration approaches used for triaxial MEMS accelerometers, which rely on local gravity, gyroscopes lack a reliable reference since the Earth’s self-rotation speed is insufficient for accurate calibration. To address this limitation, we propose a novel method that uses manual rotation of the MEMS gyroscope to a specific angle (360°) as the calibration reference. This approach iteratively estimates the sensor’s attitude without requiring any external equipment. Numerical simulations and empirical tests validate that the calibration error is low and that parameter estimation is unbiased. The method can be implemented in real-time on a low-energy microcontroller and completed in under 30 seconds. Comparative results demonstrate that the proposed technique outperforms existing state-of-the-art methods, achieving scale factor and bias errors of less than $2.5\times 10^{-2}$ for LSM9DS1 and less than $1\times 10^{-2}$ for ICM20948.

This paper presents a novel TSA mechanism with an adjustable offset between strings, which enables a variable transmission system. TSA is designed to be used in a variety of applications, including exoskeletons, and robotics. The fixed contraction range of TSAs limits their ability to provide comprehensive support. The proposed mechanism overcomes this limitation by adjusting the offset between strings. The mechanism consists of six parts with two motors. Each motor in the mechanism operates to adjust offset with only one motor and operates simultaneously to twist the strings. This enables the contraction range of TSA to be varied a wide range. Furthermore, an analytical model is also introduced for controlling the contraction range of TSA. In the experiment, the proposed mechanism shows the contraction range of TSA to be increased by up to 20%. Additionally, it showed that it is possible to vary the maximum force of TSA by up to 47%. Moreover, the analytical model has a low error. These findings suggest the promising potential for the developed TSA mechanism in a variety of applications.

Variable stiffness technologies are promising to fill the existing gap between the capabilities of robots based on soft materials and real-case applications, which may require high stiffness in specific working phases or conditions. Among these technologies, jamming transition emerged as a suitable option for devices that are intended to experience large deformations. Building upon the first version of the already introduced variable stiffness linear actuator (based on the combination of inverse pneumatic artificial muscles, fiber jamming, and positive pressure jamming), here we present the design of the VARISA, a novel multidirectional modular soft arm with tuneable stiffness. A tailored fabrication process, considered also in the design choices, is reported. Both the single module, made of three actuators, and the arm, which consists of two modules connected in series, were tested to assess deformability and variable stiffness capabilities. VARISA is 45 mm in diameter and 285 mm in length and it reached 100 mm of elongation and 82 degrees of maximum bending angle, covering a 300 mm wide workspace. Moreover, it achieved a stiffness variation close to one order of magnitude (a maximum stiffness ratio of 9.57) and, in particular, the possibility to tune the absolute stiffness between 0.06 and 0.52 N/mm in bent configuration.

In order to satisfy the high accuracy requirements of robotic applications, it is necessary to consider not only the geometric errors but also the compliance errors which are caused by the self-gravity of the link and the external payloads. A general error model is developed based on the modified Denavit-Hartenberg (DH) model. For the parameters in the error model, there is a coupling between the compliance coefficients and the link parameters, making it difficult to use only the compliance coefficients to compute the compliance errors due to external payloads. As the external payload for the robot manipulator varies, the parameter identification of the model should be conducted again. In this paper, a novel algorithm using a variable payload method is proposed to first identify the compliance coefficients using different payloads and end effector position information. Second, the kinematic parameter errors and link parameters are identified with the given compliance coefficients. Then, the algorithm generates a modified trajectory using the calibrated DH tables for the precision compensation. Simulation and experimental results demonstrate that the positioning accuracy can be improved by 80 % to 90 % even under different payloads. The root mean square, mean, maximum, and standard deviation of the residual errors by using the proposed algorithm could outperform the conventional kinematic algorithm.

Hardware-in-the-loop testing serves as a method to examine the dynamic interaction between the pantograph and catenary within controlled laboratory environments. This task involves measuring the force from the pantograph, using a real-time catenary model to determine the next pantograph position, and generating the desired pantograph movement to complete the loop. To address potential instability issues arising from communication delays and the inherent stiffness in the interaction with pantograph strips, a mass–spring system and a Linear Quadratic Gaussian controller are integrated into the system. The catenary is a finite element model of a complete section, incorporating the non-linearity introduced by dropper slackening. Validation of the results demonstrates a good level of accuracy in the HiL test approach within the frequency range of 0–20 Hz.

This paper presents an electromechanical height adjustment system for vehicles. The proposed design acts on the spring holder of the automotive suspension by means of a reversible ball-screw transmission. This actuator falls within the load-leveling category of suspensions, which becomes relevant in instances where the vehicle enters different terrains, driving conditions, or travel speeds. Furthermore, it could ease accessibility for passengers and goods. To provide reversibility, the actuation system relies on a ball-screw mechanism. It increases the overall efficiency of the actuator, which in turn, implies faster actuation times and lower power consumption. To avoid back-drivability when not needed, a highly integrated and compact locking clutch is engaged. This component provides a fail-safe feature to the suspension, even in the absence of electricity. The clutch is controlled by means of an electromagnet, which is also used to estimate its locking state by measuring the inductance of its magnetic circuit. The proposed solution is compared to a very similar irreversible configuration, where the ball-screw is replaced by a power screw with trapezoidal profile. Experiments demonstrate the benefits of the reversible ball-screw transmission in terms of actuation time (two times faster) and average efficiency (three times more efficient) when compared to the irreversible alternative. The functionality and state estimation of the locking clutch is also validated.

The problem of synthesizing a fixed-time depth observer based on monocular image feedback is considered in this study. The key challenge lies in the fact that the perspective dynamical system used to model visual motion is typically characterized as being weakly persistently exciting, which complicates observer synthesis. Furthermore, the key to achieving the task objective (safe obstacle avoidance, for instance) lies in the synthesis of a depth observer that achieves rapid convergence of the (static) obstacle depth estimate to the ground truth in a known fixed-time. To address these challenges, and in contrast with prior schemes that rely on a motion-restrictive persistency of excitation (PE) condition for ensuring exponential convergence, a novel adaptive observer framework is considered in this study that incorporates a concurrent learning (CL) term for ensuring fixed-time observer convergence. In particular, the use of concurrent learning allows for the synthesis of a relaxed finite-time excitation condition that relies on historical data recorded over a dynamic sliding window in the recent past that the proposed observer relies on to ensure fixed-time convergence. Thus, a continuous-time reduced-order observer formulation is presented that relies on camera motion data to achieve fixed-time convergence to a uniform ultimate bound for a suitably large choice of the observer gains. Experimental results are used to demonstrate the efficacy of the proposed scheme in the presence of significant measurement noise. A performance comparison study is also undertaken to demonstrate superior performance of the proposed scheme compared to leading alternative designs. Finally, the practical applicability of the proposed scheme is verified by incorporating the proposed scheme within a reactive navigation scheme to accomplish obstacle avoidance. By incorporating a suitably informative CL term within the observer framework, the proposed scheme eliminates the need to rely on a difficult-to-verify PE condition, thus rendering it more suitable for practical applications like visual target-tracking and visual servo control.