Robust control for a wheeled mobile robot to track a predefined trajectory in the presence of unknown wheel slips

In this paper, a robust controller for a nonholonomic wheeled mobile robot (WMR) is proposed for tracking a predefined trajectory in the presence of unknown wheel slips, bounded external disturbances, and model uncertainties. The whole control system consists of two closed loops. Specifically, the outer one is employed to control the kinematics, and the inner one is used to control the dynamics. The output of kinematic controller is adopted as the input of the inner (dynamic) closed loop. Furthermore, two robust techniques were utilized to assure the robustness. In particular, one is used in the kinematic controller to compensate the harmful effects of the unknown wheel slips, and the other is used in the dynamic controller to overcome the model uncertainties and bounded external disturbances. Thanks to this proposed controller, a desired tracking performance in which tracking errors converge asymptotically to zero is obtained. According to Lyapunov theory and LaSalle extension, the desired tracking performance is guaranteed to be achieved. The results of computer simulation have shown the validity and efficiency of the proposed controller

Adaptive Steering and Trajectory Control of Wheeled Mobile Robots for Autonomous Navigation

This chapter presents a new reactive navigation algorithm for a wheeled mobile robot (WMR) with a differential drive mechanism moving in unknown environments [1]. The mobile robot is controlled to travel to a predefined goal position safely and efficiently without any prior map of the environment. The navigation is achieved by modulating the steering angle and turning radius. To avoid obstacles while seeking the goal position, the dimensions and shape of the robot are incorporated to determine the set of all possible collision-free steering angles. The algorithm then selects the optimum steering angle candidate to contour the obstacle. Simulation and experimental results on a WMR prototype are used to validate the proposed algorithms

Adaptive and intelligent navigation of autonomous planetary rovers - A survey

The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

Kinematic control design for wheeled mobile robots with longitudinal and lateral slip

The motion control of wheeled mobile robots at high speeds under adverse ground conditions is a difficult task, since the robots' wheels may be subject to different kinds of slip. This work introduces an adaptive kinematic controller that is capable of solving the trajectory tracking problem of a nonholonomic mobile robot under longitudinal and lateral slip. While the controller can effectively compensate for the longitudinal slip, the lateral slip is a more involved problem to deal with, since nonholonomic robots cannot directly produce movement in the lateral direction. To show that the proposed controller is still able to make the mobile robot follow a reference trajectory under lateral and longitudinal time-varying slip, the solutions of the robot's position and orientation error dynamics are shown to be uniformly ultimately bounded. Numerical simulations are presented to illustrate the robot's performance using the proposed adaptive control law

Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

open access articleAutonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%

Comprehensive Development And Control Of A Path-Trackable Mecanum-Wheeled Robot

This paper presents an intuitively straightforward yet comprehensive approach in developing and controlling a Mecanum-wheeled robot (MWR), with decent path tracking performance by using a simple controller as an end objective. The development starts by implementing two computer ball mice as sensors to realize a simple localization that is immune toward wheel slippage. Then, a linearization method by using open-loop step responses is carried out to linearize the actuations of the robot. Open-loop step response is handy, as it directly portrays the non-linearity of the system, thus achieving effective counteraction. Then, instead of creating a lookup table, polynomial regression is used to generate an equation in which the equation later represents an element of the linearizer. Next, a linear angle-to-gain (LA-G) method is introduced for path tracking control. The method is as easy as just linearly maps the summation of two angles-the angle between immediate and desired positions and the MWR's heading angle, into gains to control the wheels. Unlike the conventional control method which involves inverse kinematics, the LA-G method is directly a displacement-controlled approach and does not require the knowledge of parametric values, such as the robot's dimensions and wheel radius. Finally, all the methods are implemented, and the MWR experimentally demonstrates successfully tracking various paths, by merely using proportional controllers