Abstract
Mobility is a significant robotic task. It is the most important function when robotics is applied to domains such as autonomous cars, home service robots, and autonomous underwater vehicles. Despite extensive research on this topic, robots still suffer from difficulties when moving in complex environments, especially in practical applications. Therefore, the ability to have enough intelligence while moving is a key issue for the success of robots. Researchers have proposed a variety of methods and algorithms, including navigation and tracking. To help readers swiftly understand the recent advances in methodology and algorithms for robot movement, we present this survey, which provides a detailed review of the existing methods of navigation and tracking. In particular, this survey features a relation-based architecture that enables readers to easily grasp the key points of mobile intelligence. We first outline the key problems in robot systems and point out the relationship among robotics, navigation, and tracking. We then illustrate navigation using different sensors and the fusion methods and detail the state estimation and tracking models for target maneuvering. Finally, we address several issues of deep learning as well as the mobile intelligence of robots as suggested future research topics. The contributions of this survey are threefold. First, we review the literature of navigation according to the applied sensors and fusion method. Second, we detail the models for target maneuvering and the existing tracking based on estimation, such as the Kalman filter and its series developed form, according to their model-construction mechanisms: linear, nonlinear, and non-Gaussian white noise. Third, we illustrate the artificial intelligence approach—especially deep learning methods—and discuss its combination with the estimation method.
Highlights
Mobility is a basic feature of human intelligence
The inertial measurement units (IMUs)-based approach derives from the inertial navigation systems (INSs) method, and the error model is used to describe the error of the position, velocity, and acceleration; in addition, the relationship between 3D and 2D video image is taken as the measurement data of the position to deduce the IMU drift error [19]
We have presented a survey of navigation and tracking for mobile intelligence of robot
Summary
Mobility is a basic feature of human intelligence. From the beginnings of robot technology, people have been imagining building a human-like machine. It is difficult to find the correct optical flow mode when the robot is moving at a high speed To overcome such a difficulty inherent in a visual sensor, other sensors have been introduced, for example, inertial measurement units (IMUs) and global positioning systems (GPSs), into the navigation system. The fact that we want robots to behave like human beings, and because a robot’s motion space and behaviors are increasingly complicated, brings great difficulties to the application of the traditional estimation-based tracking algorithms. An estimation-based algorithm has a strong theoretical foundation and can obtain good kinematic analysis of moving targets These theories are still of great value for robotics in very complicated environments. We propose an the initial idea of how to apply a current artificial intelligence (AI) method to enhance the intelligence of a robot motion system
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