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A novel normal measurement device for robotic drilling and countersinking has been developed. This device is mainly composed of three contact displacement sensors and a spherically compliant clamp pad. The compliance of the clamp pad allows it to be perpendicular to the part when the Multi-Function End Effector (MFEE) drives it to clamp the part surface prior to drilling, while the displacement sensors are used to measure the movement of the clamp pad relative to the MFEE. Once the sensors’ position is calibrated, the rotation angle of the clamp pad can be calculated by the displacement of the sensors. Then, the normal adjustment of MFEE is obtained, and the adjustment process can be achieved by the Rotation Tool Center Point (RTCP) function of robot. Thus, an innovative method based on laser tracker to identify the position of sensors is proposed.

Lane detection is one of the most important part in ADAS because various modules (i.e., LKAS, LDWS, etc.) need robust and precise lane position for ego vehicle and traffic participants localization to plan an optimal routine or make proper driving decisions. While most of the lane detection approaches heavily depend on tedious pre-processing and great amount of assumptions to get reasonable result, the robustness and efficiency are deteriorated. To address this problem, a novel framework is proposed in this paper to realize robust and real-time lane detection. This framework consists of two branches, where canny edge detection and Progressive Probabilistic Hough Transform (PPHT) are introduced in the first branch for efficient detection.

Gluing is an essential fastening step in the field of aircraft assembly except for riveting and bolting. Generally, the robotic programs of gluing are generated in CAM environment. Due to the positioning errors and deformation of the workpiece to be glued in the fixture, the nominal pose and the actual pose of the workpiece are no longer consistent with each other. The Robot trajectory of dispensing glue is adjusted manually according to the actual pose of the workpiece by robot teaching. In this paper, an on-line gluing path correction method is developed by 2D laser profile measurement. A pose calibration method for 2D laser profiler integrated into a gluing robot by measuring a fixed center point of a standard ball is proposed to identify the position and orientation of the laser sensor, which enables the accurate transforming coordinates between the robot frame and the sensor frame.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

The proper formation of fuel-air mixture, which depends to a large extend on the complex in-cylinder air flow, is an important criterion to control the clean and reliable combustion process in spark-ignition direct-injection (SIDI) engines. The in-cylinder flow vorticity field presents highly transient complex characteristics, and the corresponding vorticity field also evolves in the entire engine cycle from intake to exhaust strokes. It is also widely recognized that the vorticity field plays a key role in the in-cylinder turbulent field because it influences the air-fuel mixing and flame development process. In this investigation, the in-cylinder vortex structure and vorticity field characteristics are analyzed using the phase-invariant proper orthogonal decomposition (POD) method.

To alleviate the shortage of petroleum resources and the air pollution caused by the burning of fossil fuels, the development of renewable fuels has attracted widespread attention. Among the various renewable fuels, ethanol can be produced from biomass and does not require much modification when applied to practical engines, so it has been widely used. However, ethanol fuel has a higher heat of vaporization than gasoline, it is difficult to evaporate and atomize under cold start conditions. Besides, the catalyst has not reached the conversion temperature at this time, resulting in lower conversion efficiency. These factors all lead to higher pollutant emission levels in ethanol-gasoline blends. To solve the above problems, this research used visualization techniques to compare the effects of flash boiling and high-energy ignition technologies on the in-cylinder combustion process and pollutant emission of ethanol-gasoline blends fuel.

The continuous improvement of spark-ignition direct-injection (SIDI) engines is largely attributed to the enhanced understanding of air-fuel mixing and combustion processes. The intricate interaction between transient spray behavior and the ambient flow field is important to unveil the airflow dynamics during the spray injection process. This study investigates the fuel-spray boundary interactions under different superheated conditions by analyzing the ambient flow field pattern with constraint-based modeling (CBM). In the experimental setup, superheated conditions are facilitated by adjusting different fuel temperatures and ambient pressures. By adding the tracer particles containing Rhodamine 6G to the ambient air, the combined diagnostic of fluorescent particle image velocimetry (FPIV) and Mie-scattering is implemented to measure the velocity distribution and flow trajectory of the air surrounding the spray formation and propagation.

Nozzle length has been proven influencing fuel spray characteristics, and subsequently fuel-air mixing and combustion processes. However, almost all existing related studies are conducted when fuel is subcooled, of which fuel evaporation is extremely weak, especially at the near nozzle region. In addition, injector tip can be heated to very high temperature in SIDI engines, which would trigger flash boiling fuel spray. Therefore, in this study, effect of nozzle length on spray characteristics is investigated under superheated conditions. Three single-hole injectors with different nozzle length were studied. High speed backlit imaging technique was applied to acquire magnified near nozzle spray images based on an optical accessible constant volume chamber. Fuel pressure was maintained at 15 MPa, and n-hexane was chosen as test fuel.

High fuel injection pressure has been regarded as a key controlling factor for internal combustion engines to achieve good combustion performance with reduced emissions and improved fuel efficiency. For common-rail injection system (CRS) used in advanced diesel engines, fuel injection pressure can often be raised to beyond 200 MPa. Although characteristics of diesel spray has been thoroughly studied, little work has been done at ultra-high injection pressures. In this work, the characteristics of CRS diesel spray under ultra-high injection pressure up to 250 MPa was investigated. The experiments were conducted in an optically accessible high-pressure and high-temperature constant volume chamber. The injection pressure varied from 50 MPa to up to 250 MPa. Both non-evaporating condition and evaporating condition were studied. A single-hole injector was specially designed for this investigation.

Combustion efficiency of internal combustion engine is closely influenced by the air flow pattern in the engine cylinder. Some researchers use high-speed particle image velocimetry to visualize and measure the temporally and spatially resolved in-cylinder velocity flow fields in the optically assessable engine. However, the transparent cylindrical liner makes it difficult to accurately determine the particle displacements inside the cylinder due to the optically distorted path of scattering light from seeding particles through the curved liner. To correct for the distortion-induced error in the seeding particle positions through the optical liner, the distortion mapping function is modeled using the Gaussian optics theory. Two artificial flow patterns with 5 by 5 vectors were made to illustrate the mapping correction. Distortion-induced error of velocity vectors was precisely mapped in six different planes inside the cylinder.

Injection pressure plays a vital role in spray break-up and atomization. High spray injection pressure is usually adopted to optimize the spray atomization in gasoline direct injection fuel system. However, higher injection pressure also leads to engine emission problem related to wall wetting. To solve this problem, researchers are trying to use flash boiling method to control the spray atomization process under lower injection test conditions. However, the effect of injection pressure on the spray atomization under flash boiling test condition has not been adequately investigated yet. In this study, quantitative study of internal flow and near nozzle spray breakup were carried out based on a two-dimensional transparent nozzle via microscopic imaging and phase Doppler interferometery. N-hexane was chosen as test fluid with different injection pressure conditions. Fuel temperature varied from 112°C to 148°C, which covered a wide range of superheated conditions.

Flash boiling is considered a useful method in enhancing the liquid fuel jet break-up and spray atomization process for internal combustion engine applications. Spray atomization efficiency plays a vital role in the combustion process. Although some researches have demonstrated that flash boiling has the potential to improve the combustion efficiency and optimize emission-related issues, the effect of flash boiling spray characteristics on the combustion process has not been fully investigated. In this paper, spray characteristics and its related combustion process were studied via various non-intrusive diagnostics methods. The spray and combustion process under different test conditions were studied using an optical engine. It was found that by using flash boiling atomization, the combustion duration was reduced and IMEP enhanced significantly. Experimental results have built the relationship between flash boiling spray characteristics and the combustion performance in the engine.

Ignition performance is critical for the implementation of diluted combustion for spark-ignition engines. The short circuit and restrike phenomena can influence the initial ignition volume and discharge duration which are important for the stable ignition processes. In this study, the short circuits and restrikes of spark channels are studied with various flow velocities, spark plug gaps and discharge energies. The development of the spark channels is captured by using the direct imaging technique with a CMOS camera equipped with an image intensifier. A multi-coil ignition system is designed to enable flexible control of discharge energies. The results show that the spark plug gap size is a critical parameter to suppress the phenomena of short circuits and restrikes. With the enlargement of spark plug gap, the maximum and average lengths of the spark channel effectively increase.

This paper addresses the issues of long-term signal loss in localization and cumulative drift in SLAM-based online mapping and localization in autonomous valet parking scenarios. A GPS, INS, and SLAM fusion localization framework is proposed, enabling centimeter-level localization with wide scene adaptability at multiple scales. The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment. The IMU pre-integration information is used to provide rough pose estimation for point cloud frames, and distortion correction, line and plane feature extraction are performed for pose estimation. The map is optimized and aligned with a global coordinate system during the mapping process, while a visual Bag-of-Words model is built to remove dynamic features.

High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate.

It’s hard to achieve complete self-driving using hand-crafting generalized decision-making rules, while the end-to-end self-driving system is low in complexity, does not require hand-crafting rules, and can deal with complex situations. Modular-based self-driving systems require multi-task fusion and high-precision maps, resulting in high system complexity and increased costs. In end-to-end self-driving, we usually only use camera to obtain scene status information, so image processing is very important. Numerous deep learning applications benefit from multi-task learning, as the multi-task learning can accelerate model training and improve accuracy with combine all tasks into one model, which reduces the amount of calculation and allows these systems to run in real-time. Therefore, the approach of obtaining rich scene state information based on multi-task learning is very attractive. In this paper, we propose an approach to multi-task learning for semantics, geometry and motion.

Improvement of shift quality evaluation has become more prevalent over the past few years in the development of automatic transmission electronic control system. For the problems of the subjective shift quality evaluation that subjectivity is too strong, the standard cannot be unified and the definition of the objective evaluation index is not clear at present, this paper studies on the methods of objective evaluation of shift quality based on the multi-hierarchical grey relational analysis. Firstly, objective evaluation index system is constructed based on physical quantities, such as the engine speed, the longitudinal acceleration of the vehicle and so on, which broadens the scope of the traditional objective evaluation index further.

This paper describes a novel recognition and classification method of vehicle targets in urban road based on a vehicle-mounted Velodyne HDL64E light detection and ranging (LIDAR) system. The autonomous vehicle will choose different driving strategy according to the surrounding traffic environments to guarantee that the driving is safe, stable and efficient. It is helpful for controller to provide the efficient stagey to know the exact type of vehicle around. So this method concentrates on reorganization and classification the type of vehicle targets so that the controller can provide a safe and efficient driving strategy for autonomous ground vehicles. The approach is targeted at high-speed ground vehicle, so real-time performance of the method plays a critical role. In order to improve the real-time performance, some methods of data preprocessing should be taken to simplify the large-size long-range 3D point clouds.

In the field of automatic driving, the combination of 3D LIDAR and inertial measurement unit (IMU) is a common sensor configuration scheme in laser point-cloud localization, high-precision map making and point-cloud target detection. So it is critical to calibrate LIDAR and IMU accurately. At present, due to the large volume and high cost of 3D LIDAR with high-line-number(Such as 64 lines or 128 lines), the configuration scheme of using multiple low-line-number 3D LIDARs appears in the automatic driving vehicle sensing system. However, the common calibration methods are not suitable for multi 3D LIDARs and IMU parameters calibration on autonomous vehicle, which have the disadvantages of cumbersome implementation and low accuracy. In this paper, a joint calibration test platform composed of dual LIDARs and IMU is assembled, and a method of precise automatic calibration based on GPS/RTK data is proposed.

Lift-off length is defined as the distance from injector hole to the location where flame stabilized on a high injection pressure direct injection (DI) diesel spray. In this paper we used the high-speed (40 kHz) Schlieren and time-averaged OH chemiluminescence imaging technique to simultaneously measure the flame lift-off locations on a DI diesel spray in an optically accessible and constant-volume combustion vessel. The time-resolved development of the diesel spray acquired from the high-speed Schlieren imaging system enabled us to observe the instantaneous spray structure details of the spray flames. The OH chemiluminescence image obtained from a gated, intensified CCD video camera with different delay and width settings was used to determine the quiescent lift-off length. Experiments were conducted under various ambient temperatures, ambient gas densities, injection pressures and oxygen concentrations.