The autonomouswheeledmobile robots are very interesting subject both in scientific research and practical applications. The article deals with the fuzzy control of autonomouswheeledmobile robotic platform motion in an unstructured environment with obstacles. The simulation results show the effectiveness and the validity of the obstacle avoidance behaviour in unstructured environments and the velocity control of a wheeledmobile robotic platform motion of the proposed fuzzy control strategy.
The movement of a wheeledmobilerobot (WMR) is provided by motors, however, it is hard to control and predict the motors speed. A cascade Proportional, Integration, and Derivation (PID) controller is presented in this study to achieve the purpose of motors speed controlling. In order to test the controller, a differential drive wheeledmobilerobot (DWMR) platform is used. The platform is integrated with decision making algorithm (DMA) for the case of wall- following to test the capability of the controllers in different situations. Through the results illustrated, it shows that the cascade PID controller promises a good performance with low average error in controlling the motors to reach the desired speed.
Using bicycle model we can streamline the Ackerman steered model for a wheeledmobilerobot. We can have bicycle model by combining the left and right wheels into a pair of single wheels at the center of the front and rear axles as shown in dark black color in Figure 2. The equation of motion for the kinematic bicycle model of a car can be understood from . Before going to kinematic equation of motion one important notation we must know that is ICR (instantaneous center of rotation). We know that the regular wheel that is disk type wheels have sliding restrictions. Therefore they have zero lateral motion. As shown in figure 2, to show this geometrically we have to draw a line called as zero motion line perpendicular to the longitudinal axis of wheels shown in dotted lines. The intersection point of the zero motion line of all wheels gives a single point called ICR. Due to ICR when a robot takes turn, the wheel must be rolling along a circle such that the center this circle is our point ICR and its radius is represented by R. The radius ICR will be infinite when mobilerobot is moving in straight line. Therefore a mobilerobot like an Ackerman vehicle as shown Figure 2 may have a number of wheels, but it is essential it have a single ICR as in case of bicycle. This ICR geometric construction illustrates how robot mobility depends on the number of constraints on the robot’s motion but not the number of wheels , .
So in this project, our goal is to address the di ﬀ erent as- pects required in making an autonomousrobot recognize textual messages placed in real-world environments. Our ob- jective is not to develop new Character Recognition algo- rithms. Instead, we want to integrate the appropriate tech- niques to demonstrate that such intelligent capability can be implemented on a mobile robotic platform and under which constraints, using current hardware and software technolo- gies. Our approach processes messages by extracting char- acters one by one, grouping them into strings when nec- essary. Each character is assumed to be made of one seg- ment (all connected pixels): characters made of multiple seg- ments are not considered. Messages are placed perpendic- ular to the floor on flat surfaces, at about the same height of the robot. Our approach integrates techniques for (1) perceiving characters using color segmentation, (2) posi- tioning and capturing an image of su ﬃ cient resolution us- ing behavior-producing modules and proportional-integral- derivative (PID) controllers for the autonomouscontrol of the pan-tilt-zoom (PTZ) camera, (3) exploiting simple heuristics to select image regions that could contain charac- ters, and (4) recognizing characters using a neural network.
A two wheeled inverted pendulum (TWIP) mobilerobot is a three-degrees-of-freedom under-actuated mechanical system with highly nonlinear dynamics. This makes it a perfect test- bed for various control algorithms ranging from conventional theoretical control algorithms to intelligent control algorithms. In recent years, various control methods have been reported in literature for controlling the TWIP mobilerobot. In the early work by Ha and Yuta , a linear state feedback and feed forward controller was designed and implemented for the posture and velocity control of the TWIP mobilerobot. Grasser et al  proposed a linear state feedback controller based on pole placement technique for the robot. In  Nawawi et al a TWIP mobilerobot was developed and stabilized using pole placement controller. Kim et al  investigated the exact dynamics of the TWIP, and developed a linear quadratic regulator (LQR) controller for balancing the robot. Fiacchini et al  proposed linear and nonlinear controllers for stabilizing a personal pendulum vehicle. In Seo et al  the performance of the two wheeled inverted pendulum on tilted road was investigated, and an LQR controller was proposed for the system. Nasir et al  compare the performance of LQR and PID in stabilizing two-wheeled balancing robot. In Jones and Stol , the performance of the two wheeledmobilerobot in low- traction environment investigated by designing an LQR controller based on linearized model of the robot which includes wheel slip effects. Pathak et al  proposed velocity and position controllers for the TWIP robot via partial feedback linearization. In Li and Luo  an adaptive controller was proposed for the TWIP system which deals with model uncertainties. Huang et al  proposed three fuzzy controllers based on Takagi-Sugeno and Mamdani architectures for the balancing, traveling and steering of the TWIP mobilerobot. Kausar et al , investigated the effect of terrain inclination on the performance and stability region of a two-wheeledmobilerobot. In Muhammad et al  compare the performance of a partial feedback linearization and an LQR controller in balancing and velocity tracking control of the TWIP mobilerobot.
CANOpen communication network was formed by the CAN ports of the motor driversand the industrial control board by extending the CAN card. All data sending and receiving was realized by CANOpen network. The highest communication rate is 1Mbit/s. The block diagram of the control system of the mobilerobot is shown in Figure 7.
A robust control algorithm for tracking a wheeledmobilerobot navigating in a pre-planned path while passing through the road’s roundabout environment is presented in this article. The proposed control algorithm is derived from both the kinematic and dynamic modelling of a non-holonomic wheeledmobilerobot that is driven by a differential drive system. The road’s roundabout is represented in a grid map and the path of the mobilerobot is determined using a novel approach, the so-called laser simulator technique within the roundabout environment according to the respective road rules. The main control scheme is experimented in both simulation and experimental study using the resolved- acceleration control and active force control strategy to enable the robot to strictly follow the predefined path in the presence of disturbances. A fusion of the resolved-acceleration control–active force control controller with Kalman Filter has been used empirically in real time to control the wheeledmobilerobot in the road’s roundabout setting with the specific purpose of eliminating the noises. Both the simulation and the experimental results show the capability of the proposed controller to track the robot in the predefined path robustly and cancel the effect of the disturbances.
Classical remote control consists of many pushbuttons and big in size. It is as well as limited to specific operation as it is unprogrammable. This classic remote control become useless when the device it controls is no longer functioning, eventually become a junk. In addition, a normal pushbutton-based remote control may be too complicated to be controlled by old people and not suitable for handicapped person. Therefore, a voice controlled mobile application is introduced in this project to replace the classical remote control.
The control system involved in this project did not include multiple inputs to be processed by processor. As mentioned in the scopes of project, there are no sensors used to provide movement control to the robotic wheelchair. A simple and reliable control system has been used for this project. The robotic wheelchair developed in this project implemented medium range Programmable Integrated Circuit from Microchip technology Inc that has the appropriate features and capable to provide the controlling process.
A two-wheeled inverted pendulum (TWIP) mobilerobot is a three-degree-of-freedom under- actuated mechanical system with highly nonlinear dynamics. It is quiet challenging to control such system due to its unstable and under-actuated nature. Numerous works on modeling and control of TWIP mobilerobot have been presented in literature. Kim et al  investigated the exact dynamics of the TWIP mobilerobot, and a Linear Quadratic Regulator (LQR) controller was developed for balancing the robot. Fiacchini et al  proposed linear and nonlinear controllers for stabilizing a personal pendulum vehicle. To compensate for the measurable disturbances, the work in  compared the performance of Model Predictive Controller and LQR. Multipoint pole placement control for velocity tracking of the TWIP is shown in . In Jones and Stol , the performance of the two wheeledmobilerobot in low-traction environment was investigated by designing a LQR controller based on linearized model of the robot which includes wheel slip effects. Pathak et al  proposed velocity and position controllers for the TWIP robot via partial feedback linearization. Dai et al  proposed sliding mode controllers for self-balancing and yaw motion and designed independently. While Kim et al  investigated a nonlinear motion control using the State-Dependent Riccati Equation (SDRE) control framework. Kharola et al  discussed a fuzzy logic control strategy for control and stabilization of TWIP.
When autonomous robots navigate within outdoor environments (open space), they have to be endowed the ability to move through the environments, to move without collision with obstacles, and follow the direction with the help of (compass, GPS or camera). The basic concept of this project report is to design robot which can move to the north direction and avoid any obstacle on its way without human guidance or control. Arduino Mega 2560 was used as the “brain” to control the system of the robot [1-3, 6]. Figure 1 shows the prototype system.
Figure 8 illustrates how the robot uses the “sensing vector” and the “gap vector” to avoid the obstacle of the above example. First, the robot plans its motion toward the target in direction. The robot has continued moving (while S = 0) until the sensor LFS has detected an obstacle (LFS = 1, therefore S = 1), where the robot starts increasing its velocity in direction to reach the nearest gap which is the MRG gap. The robot has continued increasing its velocity until it has reached the maximum velocity. When the robot has reached the gap, the “gap vector” has been changed and a new nearest gap has been appeared at the front (FG = 0), in this case the robot velocity in direction will be zero and the robot keeps its motion until it reaches the condition of returning to the desired path DP (S = 0) where it starts increase its velocity in the direction to the DP (in this case, direction) until it reaches the DP.
As explained in section 3.1.2, MARVIN has inherited a SICK LMS100 laser rangefinder. Laser rangefinders, often referred to as LiDARs, use lasers to measure distance. The laser is aimed by reflecting it against a mirror. The mirror is rotated rapidly providing the laser rangefinder with a 2D field of view (FOV). The measurements are then converted into real-world values by associating the angle of the mirror with its corresponding range mea- surement. This provides polar measurements that can be converted into Cartesian coordinates that a robot’s control system can use, as explained in Chapter 4. Laser range finders often use low intensity infrared (IR) lasers to prevent damaging onlookers’ eyes. They typically have a wide FOV, fast response time, long range, high accuracy, good precision and have a small processing overhead. However they are expensive, large, heavy, high power consumption and only have a 2D planar detection area.
This work attempts to discuss the performance of a mobilerobot using the Fuzzy Logic controller that incorporates the gaussian membership for mobilerobot decision. Simulation setting in  is referred for veriﬁcation and compari- son to validate the performance between triangular and gaussian memberships. Gaussian membership is chosen as it correlates fairly fuzzy set and approximates the human sense.
The so-called tracking guidance is that the preset trajectory of AGV is divided into N reference points, and AGV always run to track the reference point not far ahead. This reference point is always in front of the AGV and not far away, but AGV can never catch up it. Through the arrangement of these series of reference points on the path, the running track of AGV is guided, and an control objective of AGV is the expected reference point not far away in front of it. Running direction of AGV can be adjusted periodically by tracking guidance method, and reduce errors generated during operation  . Kinematics model of single steering wheel AGV is analyzed as follows.
Self-balancing robot  developed as early as 1986, originated in Japan, the initial idea is to design a machine that can stand automatically. In 1986, Professor K.Y. of Nippon Telekom University conceived an automatic standing robot , but the robot can only move forward on a fixed track. In 1996, Y.S.H. and S.Y. of Tsukuba University designed and implemented a two-wheeledrobot. It uses a three-stage closed-loop control system to control the autonomous cruising of the robot in a plane . In 2002, F.G. developed the JOE . It can walk freely on slopes due to its centralized structure. In 2005, Professor R.H. at Carnegie Mellon Institute of Robotics and his team successfully developed the first ball-wheel robot, and named BALLBOT . In 2008, The Murata girl was introduced in the Japan Robot Exhibition . It maintains the lateral balance by turning the flywheel equipped in the robot body. In 2010, the REZERO was introduced by University of Zurich , which using the LQR control algorithm. This is a representative of the spherical self-balancing robot.
The mobile robots have various behaviors that could be modeled path planning, path following, Trajectory(path) tracking, adaptive goal seeking. The path tracking control of mobilerobot is an essential part of modern robots. The main task for any mobile robots is to move on a specific path. In recent years, many researchers have been dealing with researches that concern with control techniques to control path tracking. Furthermore, Many controlling techniques have been utilized, for example, sliding mode control, back stepping technique, adaptive control, fuzzy control, and neural network control, etc..(Ahmed, 2012).
Typical velocity profiles of WMR are consisting of three sections: acceleration (A), cruise (C) and deceleration (D). Certainly, there are many profile types for acceleration and deceleration depends on the motor control input. In this project, we investigate the trapezoidal velocity profile. Figure 3.8 shows the typical trapezoidal velocity profile.
restricted area during controlling vehicle on the road has been also utilized to evaluate PD-AFC algorithm. The proposed control system is implemented with and without disturbances conditions to show the robustness of the control system. In all discussed cases, the reference path is compared with PD, AFC and PD-AFC actual paths. Results shows the capability of PD-AFC to eliminate the effect of disturbances and maintain the track errors in zero level.