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.

This paper presents the design and experimental investigation of a piezo-driven monolithic compliant 3-PUU (1 prismatic joint (P-joint) and 2 universal joints (U-joints)) XYZ micro/nano manipulator with a high bandwidth frequency and a large workspace. The manipulator is manufactured using a 3D-printing technique, thus a monolithic structure is guaranteed. This feature allows the manipulator to work smoothly with high resolution, accuracy, and repeatability, and simultaneously, it avoids all the deficiencies of non-monolithic designs such as backlash, wear, and friction. Analytical and computational studies are conducted to investigate and confirm the performances of the manipulator. Additionally, an experimental study is performed to verify the analytical and computational results. The workspace is calculated using the three methods, and their relative errors along each axis are shown to validate the accuracy of the results. The tracking performances of the manipulator are validated in a feedback control strategy. Moreover , the proposed manipulator demonstrates the motion resolution of 23 nm, 24 nm, and 18 nm along X, Y, and Z axes, respectively. Finally, the experimental frequency test supports the computational results and confirms the manipulator’s high bandwidth frequency.

The current study presents three calibration approaches for the hole-drilling method (HDM). A total of 72 finite element models and 144 simulations were established to calibrate the measurements of the strain sensors. The first approach assumed the stresses acted on the boundaries of the drilled hole and thus analyzed the surrounding displacements field. The second analysis considered the loads on the outer surfaces of the specimen while measuring the strains’ differences between the model with and without the drilled hole. The third approach was more comprehensive as it considered the mechanical and thermal effects of the drilling operations. The proposed approaches were applied to two different materials (AISI 1045 and CFRP). The steel specimens were machined using a CNC lathe while the composite laminates were manufactured using the robotic fiber placement (RFP) process. Subsequently, the residual stresses (RSs) were measured using the HDM. The obtained data were compared with X-ray diffraction measurements for validation. The results showed better estimation of the RSs when utilizing the third approach and clear underestimation of the stresses using the second approach. A divergence in RSs values between the three approaches was also detected when measuring the stresses in the internal layers of the composite laminates.

In this paper, a novel six degrees of freedom (DOF) monolithic micromanipulation system is proposed and investigated. The goal of this study is to develop a parallel micromanipulator with large workspace and natural frequency utilising piezoelectric actuators (PEAs). Specific attention is paid towards designing, finite element analysis (FEA) and investigating the effect of dominant parameters on the proposed mechanism. Further, the inverse kinematic model of the proposed design is developed via FEA and is used for the study of manipulation motion and the coupling effects. Results show the performance and capability of the proposed design.

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.