Mohammadali Ghafarian

Vehicular motion simulators have evolved to become an important contributor to major industries, including defense, aerospace, and vehicle manufacturing. During the past few decades, many efforts have been made towards developing robust, adaptive motion simulators with the highest level of fidelity and realism. Thus, it is important to recollect, in order to evaluate the current state of complex robotic systems, encouraging careful planning of further improvements in the future. This article focuses on the current motion simulators’ structural designs and working principles alongside the currently developed motion control algorithms to achieve the highest fidelity. Furthermore, within the era of industry 4.0 and the fast-paced merging of technologies into key industries, some suggestions are made for future works which it is believed are worth investigating to provide robustness and adaptivity to the control of simulation systems, improving their fidelity and realism alongside reducing motion sickness experienced by the simulator operator.

A compact large-range six-degrees-of-freedom (six-DOF) parallel positioning system with high resolution, high resonant frequency, and high repeatability was proposed. It mainly consists of three identical kinematic sections. Each kinematic section consists of two identical displacement amplification and guiding mechanisms, which are finally connected to a limb. Each limb was designed with a universal joint at each end and connected to a moving stage. A computational model of the positioner was built in the ANSYS software package, hence, the input stiffness, output compliance, range, and modal analysis of the system were found. Furthermore, a monolithic prototype made of Acrylonitrile Butadiene Styrene (ABS) was successfully manufactured by the 3D-printing process. It was actuated and sensed by piezoelectric actuators (PEAs) and capacitive displacement sensors, respectively. Finally, the performances of this proposed positioner were experimentally investigated. The positioning resolution was achieved as 10.5nm {\times} 10.5nm {\times} 15nm {\times} 1.8μrad {\times} 1.3μrad {\times} 0.5μrad. The experimental results validate the behavior and capabilities of the proposed positioning system, and also verify the nanometer-scale spatial positioning accuracy within the overall stroke range. Practical applications of the proposed system can be expanded to pick-and-place manipulation, vibration-canceling in microsurgery/micro-assembly, and collaborative manipulators systems.

SLAM algorithms generally use the last system posture to estimate its current posture. Errors in the previous estimations can build up and cause significant drift accumulation. This accumulation of error leads to the bias of choosing accuracy over robustness. On the contrary, sensors like GPS do not accumulate errors. But the noise distribution in the readings makes it difficult to apply in high-frequency SLAM systems. This paper presents an approach which uses the advantage of both tightly-coupled SLAM systems and highly robust absolute positioning systems to improve the robustness and accuracy of a SLAM process. The proposed method uses a spare reference trajectory frame to measure the trajectory of the targeted robotic system and use it to recover the system posture during the mapping process. This helps the robotic system to reduce its accumulated error and able the system to recover from major mapping failures. While the correction process happens whenever a gap is detected between the two trajectories, the external frame does not have to be always available. The correction process is only triggered when the spare trajectory sensors can communicate. Thus, it reduces the needed computational power and complexity. To further evaluate the proposed method, the algorithm was assessed in two field tests and a public dataset. We have demonstrated that the proposed algorithm has the ability to be adapted into different SLAM approaches with various map representations. To share our findings, the software constructed for this project is open-sourced on Github.

Solid-State LiDAR (SSL) takes an increasing share of the LiDAR market. Compared with traditional spinning LiDAR, SSLs are more compact, energy-efficient and cost-effective. Generally, the current study of SSL mapping is limited to adapting existing SLAM algorithms to an SSL sensor. However, compared with spinning LiDARs, SSLs are different in terms of their irregular scan patterns and limited FOV. Directly applying existing SLAM approaches on them often increase the instability of a mapping process. This study proposes a systematic design, which consists of a dual-LiDAR mapping system and a three DOF interpolated six DOF odometry. For dual-LiDAR mapping, this work uses a 2D LiDAR to enhance a 3D SSL performance on a ground vehicle platform. The proposed system takes a 2D LiDAR to preprocess the scanning field into a number of feature sections according to the curvatures on the 2D fraction. Subsequently, this section information is passed to 3D SSL for direction feature selection. Additionally, this work proposes an odometry interpolation method which uses both LiDARs to generate two separated odometries. The proposed odometry interpolation method selectively determines the appropriate odometry information to update the system state under challenging conditions. Experiments are conducted in different scenarios. The results proves that the proposed approach is able to utilise 12 times more corner features from the environment than the comparied method, thus results in a demonstrable improvement in its absolute position error.

Planar 3-Degree of Freedom (3-DOF) micropositioning stages are widely adopted in many precision applications for their ability to provide in-plane rotation. However, their motion accuracy is adversely affected by cross-couplings, model uncertainty, and external disturbances. This work proposes an optimized robust control methodology based on disturbance estimation to address these issues. Systematic modeling of a 3-DOF precision micropositioning stage was utilized to develop a nonlinear disturbance observer-based sliding mode control methodology. This methodology can estimate and compensate the unavoidable cross-couplings between the major axes of motion. A stability analysis is conducted to prove the stability of the feedback system when the control approach is combined with the disturbance observer. A maximum bound on the tracking error is derived through finite-time analysis, and the parameters that affect this bound are identified. The antlion optimizer algorithm is used to optimize the control parameter based on the mean square error cost function to eliminate the requirement for manual tuning and to achieve the best attainable performance. The proposed control method is experimentally demonstrated to track complex trajectory with less tracking error than the classical sliding mode approach. The main contribution of this study is the improvement of the trajectory tracking accuracy in multi-DOF micropositioners using an optimized disturbance observer-based robust control technique.