Papers by Keyword: Sensor Fusion

Abstract: The transition of the automotive sector towards autonomous mobility requires a sophisticated amalgamation of cutting-edge hardware, redundant software frameworks, and accurate mathematical modeling. This research examines the technologies that facilitate autonomy in passenger vehicles, highlighting the shift from distributed to centralized zonal systems. The core focus of this study is the development of a unified modeling framework that integrates Kinematic and Dynamic Bicycle Models. Kinematic models prove sufficient for operations at low speeds, while dynamic models are essential for maintaining stability at high speeds, where tire slip and lateral forces gain prominence. This research provides a comprehensive classification of existing designs and introduces a mathematical framework that facilitates transitions between modeling paradigms based on the vehicle's state. A detailed numerical calculus experiment is performed, simulating the transition from a low-speed urban turn to a high-speed interstate lane change. This illustrates the necessity for an integrated modeling approach to ensure safe autonomous navigation.

Abstract: This paper presents the design, construction and development of an autonomous delivery robot aimed at structured environment such as university campuses, hospitals, residential estates and factories. The system integrates a six wheeled differential drive platform with a redesign adaptive climbing rocker-boogie suspension system. It makes use of an array of high precision sensors such as LIDAR, ultrasonic sensors, IR sensors, depth camera, real time kinematics (RTK) GPS for real time navigation and obstacle detection. The autonomous delivery robot is managed using ROS 2-based system running on an Nvidia Jetson nanoand features a mobile application for remote tracking, management and control. Simulation based testing in gazebo as well as experimental validation was conducted to evaluate the robot’s autonomous behavior and delivery performance.

Abstract: Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

Abstract: This paper deals with an intelligent multi-sensor monitoring system, which focus on the characteristic of transient occurrence in high speed grinding and its application to the machining of brittle and hard materials. Different sensors are used to collect workpiece vibration, acoustic emission, force and displacement signals, which are used to define the stability of grinding process and monitoring the fault in high speed machining. Although there is a lot of methods have been reported in recent literature for monitoring grinding process, they have not a systematic method which can totally reflect the characteristic of high speed grinding. On the other hand，no single sensor or feature has been shown to be successfully and precisely all grinding faults. This paper combined different feature selection including time-frequency domain or wavelet methods and sensor fusion based on clustering method to deal with the stability condition test in high-speed grinding. The validity of the proposed method and the excellent detection accuracy is demonstrated through tests with SiC machining in high-speed grinding.

Abstract: In this paper a low-cost Micro-Electro-Mechanical System (MEMS) inertial measurement unit is designed, a 3-axis accelerometer and 3-axis gyroscope simulated 6 degrees of freedom orientation sensing through sensor fusion. By analyzing a simple complimentary filter and a more complex Kalman filter, the outputs of each sensor were combined and took advantage of the benefits of both sensors to improved results. The experimental results demonstrate that the output signal can be corrected suitability by means of the proposed method.

Abstract:

Traditionally, Kalman Filter is used for the purpose of mixing several input signals and extracting a more reliable output, which greatly benefits aircraft navigation. This paper considers a fusion of four sensor systems: Global Positioning System (GPS), accelerometer, gyroscope and magnetometer. The resultant device, known as Starfish Main Tracking Unit (MTU), is a Flight Data Monitoring (FDM) / Tracking System equipment that uses General Packet Radio Service (GPRS) / Iridium / ICS (Internet Communications Services), which provides low cost telemetry as well as multiple solutions for global flight following and flight data transfer between aircraft and ground. Users from ground are able to monitor their fleet, configure their systems and also generate various flight reports from a single web-based interface, named the Starfish Fleet Management system. This developed system complements the Black Box by downloading limited aircraft data to the ground, provides real time tracking and assist in Search and Rescue (SAR) mission.

Abstract: This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.

Abstract: A machine intelligence assistant BCI fusion navigation method of an outdoor mobile robot is put forward in view of the problem of BCI’s low signal-to-noise ratio, bad accuracy and long time delay. A vehicle navigation system based on BCI and dual laser radar is designed and implemented. First, improved angle potential field method based on dual laser radar is used for local path planning, then with navigation intention from BCI system, control commands are generated by fusion decision and used for driving a electric vehicle with modified mechanical system. Experiments show that the system can realize intelligent obstacle avoidance and human-machine collaborative navigation based on environmental obstacle information and brain machine interface control intention and it has higher accuracy, fault tolerance and robustness

Abstract: In this paper, to release this induced errors and improve the accuracy of the measured data, a new spatial synchronization method is proposed to spatially synchronize the three-dimensional surface data sets obtained by variety surface topography measuring instruments. The proposed spatial synchronization method minimizes the geometrical error components using the data interpolation, the least squares method, and the two-dimensional cross correlation function. For verification of the method, it was applied to the measured data sets measured with a chromatic confocal microscopy, a laser scanning confocal microscopy, and an ellipsometer. Based on the experimental results, the accuracy or the proposed method is analyzed and evaluated.

Abstract: Omnidirectional mobile robot has gained popularity among researchers. However, omnidirectional mobile robot is rarely been applied in industry field especially in the factory which is relatively more dynamic than normal research setting condition. Hence, it is very important to have a stable yet reliable feedback system to allow a more efficient and better performance controller on the robot. In order to ensure the reliability of the robot, many of the researchers use high cost solution in the feedback of the robot. For example, there are researchers use global camera as feedback. This solution has increases the cost of the robot setup fee to a relatively high amount. The setup system is also hard to modify and lack of flexibility. In this paper, a novel sensor fusion technique is proposed and the result is discussed.