Path Tracking Control of a Mobile Robot by Using Dual Estimation Algorithm

Abstract

Skid-steered mobile robots have been developed to explore unknown environments, especially in rough terrain situations, where the wheel slips always occur and vary when the robot is traveling with different trajectory shapes. The wheel slippage limits the traction and braking abilities of the robot. In this paper, we propose a new dual estimation algorithm to overcome the previous limitations. The estimated values such as the robot's positions and wheel slips are obtained based on the Kalman filtering technique. As demonstrated by our experimental results, the advantages of the new method are effective to overcome the slip limitations in the path tracking control problem of a four-track wheel skid-steered mobile robot (4-TW SSMR).

Vector field path-following control for a small unmanned ground vehicle with Kalman filter estimation

“…We take account of the constraint of maximum rotation angle of steering wheel and choose an overdamped criterion. The proportional and derivative gains can be obtained from equations ( 16) and (20). Then the integral gain can be determined by using the root locus method to achieve the desired response.…”

“…To prevent actuator saturation the proportional gain k p is determined by equation ( 16). Next selecting the damping ratio z = 0:7 for the critically damped condition and calculating natural frequency v n from equation ( 19), we can obtain the derivative gain k d by equation (20). The last integral gain k i is effectively selected using root locus method to remove the possible steady-state error.…”

“…There are various sensors including inertial sensors, global positioning system sensors, visual sensors, ultrasonic sensors, and laser scanning sensors. [17][18][19][20][21][22] Because of the applicable range, limitation, and characteristics, in very rare cases only using one kind of sensors can obtain a satisfactory performance. As a consequence, multisensor fusion is a very important application that maximizes the benefits by combining the data from different kind of sensors through dynamic observer.…”

“…4 A dual estimation algorithm is proposed to estimate system states and slip parameters by integrating two Kalman filters with AHRS (attitude and heading reference system) and two incremental encoders to overcome the slip limitation for the curve path-tracking control of a four-track wheel skid-steered mobile robot in concrete terrain. 20 In this study a cost-effective sensor-fusion system composed of a low-price GPS module and 3D orientation sensor is implemented by using extended Kalman filter to estimate position for path-following guidance and control of an all-purpose wheel-based outdoor UGV.…”

An Improved Path-Generating Regulator for Two-Wheeled Robots to Track the Circle/Arc Passage

“…For circle/arc tracking problem, many approaches have been proposed, a dual estimation algorithm estimated the robot's position and wheel slips based on the Kalman filtering [4], but it is necessary to have previous knowledge about the system and measuring devices. A block iterative method known as four point-explicit group via nine-point Laplacian (4EG9L) was used for solving robot path planning problem [5].…”