Same Fuzzy Logic Controller for Two-Wheeled Mobile Robot Navigation in Strange Environments

Abstract

For any mobile device, the ability to navigate smoothly in its environment is of paramount importance, which justifies researchers’ continuous work on designing new techniques to reach this goal. In this work, we briefly present a description of a hard work on designing a Same Fuzzy Logic Controller (S.F.L.C.) of the two reactive behaviors of the mobile robot, namely, “go to goal obstacle avoidance” and “wall following,” in order to solve its navigation problems. This new technique allows an optimal motion planning in terms of path length and travelling time; it is meant to avoid collisions with convex and concave obstacles and to achieve the shortest path followed by the mobile robot. The efficiency of employing the proposed navigational controller is validated when compared to the results from other intelligent approaches; its qualities make of it an efficient alternative method for solving the path planning problem of the mobile robot.

Real-time neural control of all-terrain tracked robots with unknown dynamics and network communication delays

“…In this way, intelligent controllers can be a solution to alleviate this kind of problems, in fact fuzzy controllers have been used to deal with control of uncertain nonlinear systems (Aouf et al, 2019;Tong el al., 2020;Pillai & Suthakorn, 2019), however as has been stated in Jiang et al (2016) neural controllers are better suited to deal with complex control task. Then it is important to remark that the use of RHONN to identify the system to be controlled allow us to use any modern control approach to deal with complex control problems as realtime trajectory tracking, this cannot be done with other intelligent controllers like fuzzy systems (Jiang et al, 2016).…”

“…In Jiang et al (2016), it is presented a deep comparative analysis between the two main methodologies of intelligent control: fuzzy and neural based controllers, then in Farias (2018) it is developed the same comparison with an applicability focused to robotics, these two works illustrate the great capability of intelligent controllers to deal with unknown and unstructured environments, fuzzy controllers are mainly recommended for their simplicity to implementation, besides, fuzzy controller allows the user to include known characteristics of the system by a specialist which cannot be done with neural controllers, however, neural controllers are indicated in complex systems or tasks, where the designer has limited information about the system to be controlled. Use of intelligent control for robotics applications has been deeply studied, for example in Aouf et al (2019) and Tong et al (2020) consider implementation of fuzzy controllers for robots, in Pillai & Suthakorn (2019) it is presented a compendium of challenges in motion control of rough terrain rescue robots. In Rios et al (2017), an implementation of the NIOC scheme applied to a modified HD2® (HD2 is a registered trademark of SuperDroid Robots) is presented, such work presents the simulation and experimental results as well as a comparison of the NIOC scheme with a super twisting scheme.…”

Navigation Simulation of a Mecanum Wheel Mobile Robot Based on an Improved A* Algorithm in Unity3D

“…Based on the information about the obstacles, the working environment of a robot can be categorized as a completely known environment, a partially known environment, or a completely unknown environment. It can also be categorized as a static environment or a dynamic environment [50,51,52]. There are many path planning and navigation algorithms, such as PRM, RRT, EST, RRT*, APF, MPC, ANN, GA, PSO, ACO, and D* [53], compared to which the A* algorithm has advantages such as its simple principles, easy realization, and high efficiency.…”

Challenges for Novice Developers in Rough Terrain Rescue Robots: A Survey on Motion Control Systems

“…This feature enables rescue robots to carry out sophisticated operations. Currently, the motion control systems of most robots are highly advanced [15]. In most cases, these motion control systems are also integrated with vision and force systems.…”