Rapid Navigation Function Control for Two-Wheeled Mobile Robots

Abstract

This paper presents a kinematic controller for a differentially driven mobile robot. The controller is based on the navigation function (NF) concept that guarantees goal achievement from almost all initial states. Slow convergence in some cases is a significant disadvantage of this approach, especially when narrow passages exist in the environment and/or specific values of design parameters are set. The main reason of this phenomenon is that the velocity control strongly depends on the slope of the NF. The algorithm proposed in this paper is based on a method introduced in Urakubo (Nonlin. Dyn. 81(3): 1475-1487 2015), that extends NF to nonholonomic mobile platforms and allows stabilizing not only the position of robots but also their orientation. This algorithm is used as a reference in experimental performance comparison. In the new algorithm, the gradient of the NF is used to generate motion direction but the velocity is computed as a function of position and orientation errors. This approach results in much better state converge. Analysis of the convergence shows how the location of the eigenvalues of linearized system affects time of goal achievement. The paper describes saddle point detection and avoidance methodology and presents their experimental verification. It also shows what happens in practice if initial position is located exactly in the saddle point and its detection/avoidance procedures are turned off.

Output optimization of scalar and 2‐dimension time‐varying nonlinear systems using zeroing dynamics

“…Performance of output optimization of 2DTVN system (26) equipped with the DD3 and ZD3 controllers in Example 4 the mobile robot do not skid sideways or float [36]:…”

“…The motion model of the 2‐wheeled mobile robot is shown in Figure 6(A). Then, the mathematical motion model can be derived on the basis of the assumption that the wheels of the mobile robot do not skid sideways or float [36]: where and denote the positions of the 2‐wheeled mobile robot; and represent the translational velocity and angular velocity of the 2‐wheeled mobile robot, respectively; is the orientation of the 2‐wheeled mobile robot. Besides, the smooth desired and are defined.…”

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

“…Khairunizam et al 30 carried out fuzzy membership function controller with inputs as motion information from the movements of two-wheeled robots and obtained the forward velocity of mobile robot as output while traversing from source to target. Kowalczyk 31 proposed a navigation function controller which generates the motion direction of mobile robot and determines the velocity of robot during movement or turning while heading toward the target. Shayestegan and Marbaban 32 developed a new Braitenberg strategy for smooth navigation of mobile robot.…”