Jae-Eun Park

LiDAR-vehicle external calibration involves estimating the relative pose of the LiDAR body from the ego-vehicle coordinate frame, which is required after the LiDAR is assembled on the vehicle. Current methods necessitate direct wiring to the LiDAR controller unit to access 3D point cloud data. However, situations can limit a third party’s access to the LiDAR control unit for security and other practical reasons. Can the LiDAR-vehicle pose be estimated under such circumstances? Therefore, this study addresses this issue by proposing a new approach to the LiDAR extrinsic calibration method that does not require accessing 3D point cloud data. The proposed solution uses a designed camera-target board system to capture images of LiDAR beams projected on a cylindrical target. By utilizing geometrical modeling equations, it can estimate the relative 6D pose of the LiDAR body on the reference frame. Experiments demonstrated accuracy within 0.05 degrees and 0.7 mm. This one-shot LiDAR-vehicle alignment inspection system when accessing 3D point cloud data is restricted for various practical applications.

This study proposed a standalone module of a tunable vibration absorber (TVA) using a self-sensing magnetorheological elastomer (MRE) that replaced accelerometer sensors required for the automatic tuning of a TVA. The manufactured self-sensing MRE can estimate the frequency of the input vibration and change its stiffness to tune the absorber system. Unlike previous MRE-based TVAs, the proposed TVA module is free from external sensors and is designed as a compact system to be easily attached to the target application, such as a linear compressor. In this paper, a self-sensing MRE of 10 mm in thickness was manufactured for rigid support of the absorber mass, allowing the electrical resistance to change by 1,375% from 20.6 to 283 kΩ owing to a compression strain of 2.5 mm. In addition, test results showed that the self-sensing MRE can estimate the vibration frequency within the error of 0.04 Hz. Under a magnetic field of 232 mT, the designed self-sensing MRE achieves a stiffness variation of approximately 125% for tuning the absorber frequency from 39 to 45 Hz. Thus, based on a series of experiments, the vibration sensing and control performance of the proposed self-sensing MRE TVA was validated.

This paper proposes a novel model-free reinforcement learning (RL) for vibration control of a magnetorheological elastomer (MRE)-based application. Because the modeling of the MRE stiffness is nonlinear and time-varying depending on various environmental factors, this paper approached the MRE control issue via a model-free learning based method. In this study, an RL model is designed for the MRE-based tunable vibration absorber (TVA) which can control the optimal stiffness of MRE to maximize the vibration suppression. The designed RL algorithm continuously learns and updates the optimal control input of the MRE stiffness adaptively to the dynamic environment without using any prior knowledge of the MRE modeling. From analyzing the mechanism of MRE TVA, the RL algorithm and parameters are carefully designed for a high vibration performance. Also, this study proposed several ideas to make the RL model simpler and converge at a high rate. The experiments confirmed that the proposed RL model showed a rapid convergence to the optimal policy which could minimize the vibration level with respect to the dynamic excitation disturbance. Results showed that the RL model had a similar performance as the conventional tuning method and suppressed the vibration level as much as 57% compared to the one without the controller. Also, the proposed RL algorithm was able to estimate the actual dynamics of the MRE TVA by learning from the environment. Thus, this study showed the feasibility of implementing a model-free RL model to realize an adaptive controller for applications based on highly nonlinear MRE.

This study designs an all-in-one module of a tunable vibration absorber (TVA) using magnetorheological elastomer (MRE) considering easy installation in a plug-and-play concept on an actual industrial plant. The proposed system is designed to overcome several limitations on a typical system composed of an electromagnet and MRE TVA, such as environmental robustness, bulky size, high power consumption and complex installation on an actual plant. The designed TVA module contains sensors for MRE tuning control feedback, an energy-efficient magnetic field density generator, MRE and absorber mass. A novel but simple magnetic field generator is proposed using a small brushless DC gimbal motor and permanent magnets to reduce unnecessary energy consumption for long-term operation. The TVA module is designed so that it can be easily attached to an existing target system, such as a linear compressor. Using the embedded sensors in the module, it can automatically tune the MRE stiffness adaptively to the disturbance vibration on the target system. The prototype performance is validated with experiments showing that it can provide approximately 94% of stiffness change and 58% of vibration suppression for disturbance in the frequency range of 51.6–71.9 Hz.