Mechatronics最新文献

Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.

This study presents a 3D object detection technology for mobile platforms and its application. Rather than an innovative high-performance model, we proposed a “useable” model for the robot industry at the current technology stage by combining various techniques. To reduce computation time, a 2D region proposal was obtained using a RGB image-based CNN model. By applying the DBSCAN clustering technique to the point cloud corresponding to the 2D region proposal, a method of obtaining a 3D region proposal was proposed. This allowed for 3D object detection using an RGB image dataset, which has been widely researched, while reducing the computation load to a level suitable for use in mobile robots. Furthermore, the 3D object detection was integrated into a ROS 2-based mobile platform, which was used to perform pedestrian-safe avoidance tasks and elevator button operation tasks. The performance was confirmed through experiments.

Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are integrated. To address this challenge, a modular redesign framework is proposed in this paper. Herein, first, allowed changes in the dynamics (represented by frequency response functions (FRFs)) of the redesigned system are defined with respect to the original system model, which already satisfies system specifications. Second, these allowed changes in the overall system dynamics (or system redesign specifications) are automatically translated to dynamics (FRF) specifications on module level that, when satisfied, guarantee overall system dynamics (FRF) specifications. This modularity in specification management supports local analysis and verification of module design changes, enabling design teams to work in parallel without the need to iteratively rebuild the system model to check fulfilment of system FRF specifications. A modular redesign process results that shortens time-to-market and decreases redesign costs. The framework’s effectiveness is demonstrated through three examples of increasing complexity, highlighting its potential to enable modular mechatronic system (re)design.

This paper presents a mobile supernumerary robotic approach to physical assistance in human–robot conjoined actions. The study starts with the description of the SUPER-MAN concept. The idea is to develop and utilize mobile collaborative systems that can follow human loco-manipulation commands to perform industrial tasks through three main components: (i) an admittance-type interface, (ii) a human–robot interaction controller and (iii) a supernumerary robotic body. Next, we present two possible implementations within the framework — from theoretical and hardware perspectives. The first system is called MOCA-MAN, and is composed of a redundant torque-controlled robotic arm and an omni-directional mobile platform. The second one is called Kairos-MAN, formed by a high-payload 6-DoF velocity-controlled robotic arm and an omni-directional mobile platform. The systems share the same admittance interface, through which user wrenches are translated to loco-manipulation commands, generated by whole-body controllers of each system. Besides, a thorough user-study with multiple and cross-gender subjects is presented to reveal the quantitative performance of the two systems in effort demanding and dexterous tasks. Moreover, we provide qualitative results from the NASA-TLX questionnaire to demonstrate the SUPER-MAN approach’s potential and its acceptability from the users’ viewpoint.

Ensuring driving stability in wheeled mobile robots (WMRs) within dynamic environments is crucial for reliable navigation. This study presents the design and testing of a double-wishbone suspension (DWS), which is specifically tailored for a highly maneuverable six-WMR configuration, to address stability challenges in unstructured terrains. During the suspension design phase, critical factors such as the link length, position of shock absorber, spring and damping coefficients, and roll center location were optimized using the non-dominated sorting genetic algorithm (NSGA). The proposed DWS module ensures robust and stable driving performance for medium-sized WMRs. It effectively reduces rollovers and external shocks on uneven terrains while maintaining consistent traction across all wheels. Unlike current applications of the DWS in robotics, all the optimized parameters of the DWS with the NSGA algorithm are tailored for high-speed travel and are proficient at absorbing impacts that are encountered during outdoor driving. For practical implementation, a fabricated platform with optimal design parameters was subjected to field tests to evaluate its driving performance, both in prolonged driving on a circular route and in outdoor settings, with bumpy obstacles. The study presents a comprehensive stability analysis of the DWS and the proposed mobile robot, with a specific emphasis on rollover scenarios. The experimental results unequivocally demonstrated that the six-WMR equipped with the proposed DWS outperforms its counterpart without the DWS. This study highlights the reliability of the proposed DWS in the six-WMR configuration for efficient outdoor operations in unstructured terrains.

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.