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Research on the steering characteristics of the crawler robot based on the gyroscope
Published in Yigang He, Xue Qing, Automatic Control, Mechatronics and Industrial Engineering, 2019
H.H. Liu, C.S. Ai, Y.Q. Bao, G.C. Ren, Y.C. Du
With the continuous development of mobile robot technology, the application range and functions of mobile robots have been greatly expanded and improved and have been widely used in the fields of industry, agriculture, national defense, medical care, rescue, and service. There are many types of mobile robots, and the movement methods of the robot can be divided into wheel type, leg type, led-wheel type, crawler type (Huang et al., 2015) and so on. Among them, the crawler robot is different from the general wheeled or leg robots. It has the advantages of small grounding pressure, wide supporting area and better maneuvering characteristics (Chen & Chen, 2007) and it is suitable for working in complex environments such as soft ground and muddy roads. But achieving accurate steering control of crawler robots is still a technical problem when the crawler was used as the robot moving chassis. Therefore, it is of great significance to study the steering control of crawler robots.
Introduction to Artificial Intelligence and Soft Computing
Published in Konar Amit, Artificial Intelligence and Soft Computing, 2018
Navigational Planning for Mobile Robots: Mobile robots, sometimes called automated guided vehicles (AGV), are a challenging area of research, where AI finds extensive applications. A mobile robot generally has one or more camera or ultrasonic sensors, which help in identifying the obstacles on its trajectory. The navigational planning problem persists in both static and dynamic environments. In a static environment, the position of obstacles is fixed, while in a dynamic environment the obstacles may move at arbitraiy directions with varying speeds, lower than the maximum speed of the robot. Many researchers using spatio-temporal logic [7–8] have attempted the navigational planning problems for mobile robots in a static environment. On the other hand, for path planning in a dynamic environment, the genetic algorithm [23], [26] and the neural network-based approach [41], [47] have had some success. In the near future, mobile robots will find extensive applications in fire-fighting, mine clearing and factory automation. In accident prone industrial environment, mobile robots may be exploited for automatic diagnosis and replacement of defective parts of instruments.
Landmarks and Triangulation in Navigation
Published in Shuzhi Sam Ge, Frank L. Lewis, Autonomous Mobile Robots, 2018
Huosheng Hu, Julian Ryde, Jiali Shen
Autonomous mobile robots need the capability to explore and navigate in dynamic or unknown environments in order to be useful in a wide range of real-world applications. Over the last few decades, many different types of sensing and navigation techniques have been developed in the field of mobile robots, some of which have achieved very promising results based on different sensors such as odometry, laser scanners, inertial sensors, gyro, sonar, and vision [4]. This trend has been mainly driven by the necessity of deployment of mobile robots in unstructured environments or coexisting with humans. However, since there is huge uncertainty in the real world and no sensor is perfect, it remains a great challenge today to build robust and intelligent navigation systems for mobile robots to operate safely in the real world.
Teams of robots in additive manufacturing: a review
Published in Virtual and Physical Prototyping, 2023
Abdullah Alhijaily, Zekai Murat Kilic, A. N. Paulo Bartolo
Mobile robots design challenges: The use of cooperative mobile robots is an emerging area in AM. However, these systems require better positioning and localisation methods (Tiryaki, Zhang, and Pham 2020). Odometry data is not enough for a mobile robot to have accurate positioning since errors accumulate over time (Ganganath and Leung 2012). Thus, it is necessary to consider multiple sensors such as inertial measurement units, cameras, ultrasonic beacons, and laser range scanners. Fusing sensors’ measurement increases the accuracy of the AM system, but more data needs to be stored and processed. However, the required accuracy and precision depend on the application, for example, material extrusion with small nozzle diameters requires higher precision than those with larger nozzles such as in concrete 3D printing. Furthermore, there are practical considerations that need to be considered, for example, the developer needs to decide between cables and batteries. Cables limit the workspace and the motion between robots while batteries limit communication means to wireless only. Moreover, the capacity of the batteries limits the printing time unless a self-charging robot is used, which further increases the costs and complexity of the system. Cables further complicate the task of collision avoidance. Mobile robots also face challenges in terms of power consumption and material stock refilling. No work currently implemented automatic material stock refilling for mobile robots.
Attitude stability control system of mobile robot mechanism based on nanosensor
Published in Journal of Control and Decision, 2023
Dongfang Song, Hong Ji, Guanfei Yin
A mobile robot is a software-controlled machine that uses sensors and other technologies to detect and navigate its environment. Mobile robots combine artificial intelligence (AI) with actual robotic parts like wheels, tracks, and legs to function. The mobile platform of a mobile robot working in an unstructured environment is often in an unbalanced state, which makes the precision instruments and video images on the robot shake violently, which cannot complete the specified tasks well (Lombal et al., 2020; Luo et al., 2020; Wang, Wang, et al., 2019). For the mobile robot studied, it can adjust the posture of the mobile robot platform by automatically adjusting the joint angles of four independent control legs. When the robot moves on the ground, the robot can automatically adjust its posture when the platform is not level.
Trajectory tracking with time-varying terrain conditions for an autonomous omnidirectional mobile robot using stratified variable structure saturated control
Published in International Journal of Systems Science, 2020
Chih-Lyang Hwang, Yunta Lee, Wei-Hsuan Hung
Recently, the tasks of human–robot interaction or collaboration, such as home automation, surveillance, health-care-related, and unmanned transportation tasks, are designed and developed (Lin, 2017). In these tasks, trajectory planning, obstacle avoidance, trajectory tracking, and navigation of mobile robots have become popular topics in the robotic field (Chung et al., 2012; Hou et al., 2018; Hwang et al., 2018; Hwang & Fang, 2016; Kayacan et al., 2016; Kim & Song, 2014; Li et al., 2012; Pu et al., 2018). There are many types of mobile robot, e.g. unicycle, differential, car-like, (active) omnidirectional, tractor-and-trailer types, multi-segment continuum robot, and marine surface vessels (Barreto et al., 2014; Fu et al., 2019; Huang, 2013; Huang et al., 2015; Hwang, 2016; Hwang et al., 2018; Hwang & Lee, 2018; Kim & Kim, 2014; Ma et al., 2014; Ren& Ma, 2015; Rotondo et al., 2015; Roy et al., 2017; Zhang & Liu, 2014). Each type has different kinematics, affecting the result of motion control and trajectory planning (Shen et al., 2018;Yuan et al., 2019), and applications (for balance, Han & Lee, 2015) of a mobile robot or vehicle. One of the challenges for the faster trajectory tracking control of omnidirectional mobile robot is how to design an effective and practical control in the presence of time-varying terrain and uncertainties including variational system function and output disturbance.