Mechatronics最新文献

The planar tape-spring hyper-redundant manipulator presented in this paper is mainly constructed from tape springs, fixed-drive components, and mobile-drive components. It not only has high robustness and excellent transformability, but also high packaging efficiency. However, when the manipulator extends to a long range in motion experiments, some segments of the tape springs buckle. To address this drawback, a kinematic model of the planar tape-spring hyper-redundant manipulator is established, and, a configuration planning method based on a virtual spring model is proposed to solve the inverse kinematics problem. To enhance stability, the column stability is then incorporated into the configuration planning model. This approach relies on only configuration planning to prevent buckling. An alternative approach of adding auxiliary rods into the manipulator is also proposed. With this method, extra intermediate supports have been added to the manipulator. The effective column length of some segments is shortened, which effectively increases the critical buckling load of those segments of the tape spring. Finally, a prototype was subjected to motion and stability experiments to validate the presented approaches and analysis.

A cable-driven manipulator demonstrates significant application in cramped environments, such as space maintenance and equipment monitoring, owing to its slender body and excellent flexibility. However, in traditional designs, the mapping between the operational space and the joint space is nonlinear and non-consistent, and the driving cables are also coupled. Consequently, the kinematics and dynamics become highly complex, posing challenges in enhancing efficiency and precision in trajectory planning and control. This paper introduces a novel linear decoupling cable-driven manipulator with independent driving joints. Two sets of nonlinear transmission mechanisms are designed and serially connected to form an equivalent linear transmission mechanism. This arrangement establishes a proportional relationship between the motor angle and joint angle, with the proportionality coefficient representing the equivalent transmission ratio. Moreover, a two-way wire-pulling mechanism is designed to achieve one-to-one driving between the motor and the joint. The nonlinear coupling problem between driving cables is solved by connecting the driving cable to the target joint through a constant-length cable sleeve. Based on the aforementioned design, the linear and consistent mapping between the operational space and the joint space is realized, significantly simplifying the kinematic model. Prototype experiments validate the manipulator's extensive range of motion and high motion accuracy.

In recent years, there has been significant progress in developing specialized 3D printing techniques that cater to various demanding applications. However, the current state of this technology is challenged when it comes to complex in situ printing scenarios, which require a controlled printing platform. The lack of a stable printing platform is a fundamental limitation of its use in in situ applications. To address this issue, we present a novel platform-independent 3D fabrication process that enables printing on platforms with non-cooperative movement. The process overcomes the challenge of high-speed tracking, motion compensation, and real-time printing by developing a closed-loop visual feedback-controlled robotic printing process. The proposed process incorporates a marker-based visual detection and tracking controller setup, which is discussed in detail. The algorithm consists of two loops running asynchronously: a high-speed inner control loop and an outer measurement loop. This setup enables precise and accurate tracking of the printing platform, compensating for any disturbances during the printing process. Our experimental results demonstrate the successful printing of simple linear geometries, even with low-disturbing platform velocities. Moreover, the tracking controllers' ability to handle measurement occlusion is validated, showing the proposed process's robustness and effectiveness. Our work provides a significant step towards enabling 3D printing in complex in situ printing scenarios.

This article presents an experimental validation of a model predictive path-following control algorithm (PF-MPC ) applied to a truck–trailer system, encompassing both forward and backward motions. The proposed controller is designed to precisely follow a predefined path generated by a path planner, with a designated guidance point positioned on either the truck or the trailer. The algorithm’s performance is assessed through implementation and validation on a model-scaled truck–trailer system, where MPC, state estimation, and low-level control are executed on a microcontroller (MCU ). The experimental results demonstrate the effectiveness of the proposed control approach in achieving highly accurate path-following performance, even when operating in the challenging context of unstable backward motion, and with the involvement of up to two trailers. Moreover, the successful implementation of the algorithm on a microcontroller underscores its suitability for real-time control applications. The results of this study collectively highlight the promising potential of the proposed control algorithm for practical utilization in autonomous driving systems.

Fluctuations in oil temperature, changes in friction plate temperature, and friction plate wear significantly influence the precision of torque control in wet clutches, consequently impacting vehicle launch and gear shift quality. In this study, we introduce a novel approach for estimating the dynamic friction coefficient of wet clutches and develop a feedforward torque controller tailored to dual-clutch transmissions. This controller adeptly compensates for the effects of oil temperature variations, friction plate temperature shifts, and wear. We also incorporated an observer for real-time estimation of clutch torque. The dynamic friction coefficient of the clutch is continuously estimated using models that account for the influence of oil temperature, friction plate temperature, and service mileage. Leveraging this estimated dynamic friction coefficient, the clutch torque is precisely controlled during slip engagement. Our co-simulation results affirm the accuracy of the controller presented in this paper. Even after changes in factors affecting friction coefficients, it consistently maintains control precision, surpassing non-adaptive controllers based on pressure-torque and pressure-speed difference-torque models. Bench testing further validates the controller's accuracy in torque control and its adaptability to fluctuations in oil temperature, friction plate temperature, and wear.

In large-scale factories and warehouses, power-assisted carts are effective in transporting cargo to reduce the burden on workers. Herein, we developed a force-sensorless power-assisted cart system that can detect the operating force applied by the operator without using a force sensor and control the power-assisted drive. In this system, the operator directly grasps and operates the body of the cart, and the applied force is estimated from the cart body sway caused by the operation. The transport cart is controlled based on the estimated hand force. In such closed-loop systems formed by humans and machines, mechanical interactions affect the operability of the equipment. In this study, we experimentally verified the power-assisted manual operability of the proposed force-sensorless power-assisted transport cart. We examined the accuracy of the path tracking in manual operation experiments with 20 subjects and analyzed the results of the subjective evaluation. Based on the experimental results, the influence of the control parameters on operability was considered, and design guidelines were presented.

Mimicry-based teleoperation has been widely used in various fields due to its superior scalability. Converting the mapping information of the human arm to the robotic arm is a key problem in teleoperation. However, existing methods only study how to map the motion information but do not consider the performance of the robotic arm after mapping, which often leads to task failure due to the substandard performance of the robotic arm. Therefore, for a class of robotic arms with wrist structures, a new kinematic mapping method is proposed to enable the robotic arm to meet the requirements of the index in real-time. First, an index space module is built to efficiently solve the index space containing all mapping information and indexes. Then, the mapping model solving module is established, which can make the robotic arm meet the index requirements when following the human arm. On this basis, the kinematic mapping module is proposed to convert the mapping information into the joint angle of the robot arm. Finally, numerical simulations and physical prototype experiments are conducted to verify the effectiveness of the proposed method.

The increasing complexity of next-generation mechatronic systems leads to different types of periodic disturbances, which require dedicated repetitive control strategies to attenuate. The aim of this paper is to develop a new repetitive control strategy to completely attenuate a periodic disturbance and a user-defined number of relevant higher harmonics with limited memory usage. To this end, a multirate repetitive controller is developed, which combines a buffer at a reduced sampling rate with learning and robustness filters at the original sampling rate of the system. This leads to a linear periodic time-varying system, for which convergence conditions are developed. The method is implemented on an industrial print-belt system, demonstrating that it can match the performance of traditional repetitive control while significantly reducing the memory usage.

Exploring tasks in unknown environments has become a relevant search and rescue robotics approach. Ground robots are a better alternative to rescuers for first exploration. However, exploration progress is often limited by uneven terrains that exceed the kinematic capabilities of robots, including those with complex locomotion systems. This work proposes an innovative solution based on collaborative behaviours to overcome even terrains. A method employing two collaborative robots designed to operate in a marsupial configuration to surmount uneven terrains has been implemented. These robots, denoted as R1 (enhanced with a mobile ramp) and R2 (serving as an explorer), interact synergistically to expand the explored area autonomously. A state machine has been implemented to manage the progression of the mission, based on a perception (RGB-D) system, for both decision-making and autonomous execution of the process. In the initial stage, the terrain and ascent zones to be explored are characterized using point clouds and unsupervised learning. Subsequently, the second stage manages the interaction between the robots by controlling the R2 ascent through the R1 ramp using artificial vision algorithms and beacons. Outdoor tests have been performed to validate the method. The main results show an effectiveness of 95% in automatically identifying access zones.

In this paper, a novel adaptive hybrid-mode assist-as-needed (AHMAAN) control algorithm is designed for the exoskeleton-assisted upper limb rehabilitation training. The overall control framework includes an outer control loop to calculate the required interaction force, and an inner control loop to drive the exoskeleton track subject’s motion, and provide specific target interaction force obtained from the outer control loop. In the outer control loop, a hybrid control mode is proposed, which consists of resistive mode and assistive mode. In this regard, a virtual tunnel is firstly established around the defined training task path, and the training mode is switched according to the deviation between the subject’s position and the boundary of the virtual tunnel. Furthermore, for tuning the strength of the resistance or assistance to the subjects with different motor capabilities, two adjustable gain factors are designed, whose values are adaptively adjusted according to the subject’s training performance by using a fuzzy logic. Then, for the inner control loop, a barrier Lyapunov function-based controller is designed to constrain exoskeleton tracking errors within the defined boundary. Meanwhile, time delay estimation (TDE) technology is used to estimate the uncertain terms of the system, and a robust adaption law is developed to compensate TDE error. Experimental tests have been performed on an upper limb exoskeleton with three healthy subjects to evaluate the effectiveness of the developed method. The results show that the proposed control scheme can be effectively applied in a variety of rehabilitation requirements and achieves better training performance than classical hybrid-mode assist-as-needed control method.