Having seen that the robotic and AI communities are progressing slowly in this area relative to the computer technology, a new approach has emerged in the mid 1980’s. This approach redefines the intelligence  and tries to mimic the animal behaviors in basic, modular levels on the reactive robotics foundations [2, 3]. The name for this approach is BehaviorBased Robotics and it is defined in chapter 2, starting from the biological inspirations [28-30], continuing on the first studies and theoretical foundations of the approach [1-3] and extended with Emotion Based Architecture [5-7]. The recent studies are on the hybrid architectures having interaction between the upper deliberative levels and lower level behaviors. The behaviors are used in the lower levels as reactive modules and some deliberative actions or long-term planning are done on the upper levels that are managing the behaviors.
A large number of researchers have used kinematic models to develop motion control strategy for mobile robots, their argument and assumption that these models are valid if the robot has low speed, low acceleration and light load . Dynamic modeling takes into account the forces acting on the vehicle. This model can constructed using the no-slip condition  or allowing wheel slip . In either case, the acceleration of the car is considered. In dynamic modeling the vehicle’s dynamic properties, such as mass, center of gravity, etc. enter into the equations. to drive this model, the nonholonomicconstraints of the system are utilized .Dynamic equation of wheeled mobilerobot is described as: M(q)W+C(q,q)w+Dw=τ (7)
Abstract: This study proposes behavior-based navigation architecture, named BBFM, for mobilerobot in unknown environment with obstacles. The architecture is carried out in three steps: (i) analyzing the navigation problem to determine parameters of the architecture; (ii) designing the objective functions to relate input data with the desired output; and (iii) fusing the output of each objective function to generate the optimal control signal. We use fuzzy logic to design the objective functions and multi-objective optimization to find the Pareto optimal solution for the fusion. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behavior- based architectures in navigating the mobilerobot in complex environments.
The main task of motion control of mobilerobot is to achieve the goal while avoiding obstacles. For this purpose we propose to use some multiple motion modes, namely, “to the target”, “obstacle avoidance”, “along the right wall” and “along the left wall”. The implementation of each motion mode is carried out by using a method based on fuzzy logic. Using multiple motion modes provides the possibility of parallel computation values of various motion modes and the ability to easy adapt a mobilerobot to perform other tasks which are carried out by adding extra modes that implemented the required behavior. For motion modes “obstacle avoidance”, “along the right wall” and “along the left wall” the input linguistic variables are distances to obstacles as measured from the front side, the right side and the left side of a mobilerobot. For motion mode “to the target” the input linguistic variable is an angle error that is calculated as the difference between the desired heading required to reach the goal and the actual current heading. The output linguistic variable of each motion mode is rotation angle of a mobilerobot. The input signals are received from sensors, which are placed in the front, right and left sides of the mobilerobot and a machine vision system. The main problem of using multiple motion modes is to obtain the resulting rotation angle of mobilerobot, because the different output data can be received from different motion modes. That is necessary to determine the most effective rotation angle of mobilerobot using the obtained rotation angles of different motion modes. We propose to calculate some coefficients which express the degree of activation of each motion mode for the determination of the effective rotation angle of mobilerobot. Calculating the activation coefficients is carried out by using method based
available information effectively. For many practical problems, however, an important portion of information comes from human experts which is usually not precise and is represented by fuzzy terms like small, large, not very new, and so on. In addition, in controlling complex systems such as mobilerobot navigation, we are faced with the problem of inadequate modeling of the systems, a large quantity of uncertain sensory measurements that are difficult to interpret accurately, and lacking efficient computations of control actions to achieve a desired performance of the systems. This means that, effective control of mobile robots and their associated sensors demands the synthesis and satisfaction of several complex constraints and objectives in real-time, particularly in unstructured, unknown, or dynamic environments such as those typically encountered by outdoor mobile robots. The publication of Professor Zadeh (Zadeh, 1 965) on fuzzy sets has spurred a great interest in the development of fuzzy logic controllers as an alternative to existing advanced model-based controllers for controlling such
The central control command unit augments the preferences that each command receives from the behaviors and selects the command that gets the highest score. The behaviors are treated as advisors to the central control command unit; they together form an advisory block. Since each behavior has to express its relative preference to each of the available command alternatives, it respond by firing multiple signals, one corresponding to each of the available command alternatives. Fig.1 shows the basic structure of preference –basedfuzzybehavior system. In a preference –basedfuzzybehavior system each behavior is a fuzzy system and the preference levels are expressed as fuzzy sets. The preference basedfuzzybehavior system is inspired by the result of [1, 11, 12]. The work of  served to firmly establish a rigorous generalized framework for the development of similar method to their navigational algorithm first demonstrated in . The Distributed Architecture for Mobile Navigation (DAMN) and
Because the wheel-basedrobot has some constraint in mechanism, they can not move around well. In this paper, we design and implement of an omni-directional spherical mobilerobotcontrol system. The mobile mechanism of spherical robot is different from the wheel-based one. The major advantage of this spherical robot is that can move for omni-directional with no constraint. It is obviously such a robot system is high nonlinearity and is always unknown. It is difficult to establish an exact mathematical model for the design of a model-basedcontrol system. To dealing with such an unknown nonlinearities and external disturbances, the technique of fuzzy logic control is introduced. The fuzzy logic control is a complete difference approach that does not require a precise mathematical model of the system. This control method is based on human experience to understand the behavior of the system. Thus, control design is simple than traditional one. Recent years, there have been many researches about the intelligent control for complex nonlinear system -.
Navigation and obstacle avoidance are very important issues for the successful use of an au- tonomous mobilerobot in a dynamic and unstructured environment. Mobilerobot researchers aim to build an autonomous and intelligent robot which can plan its motion in a dynamic en- vironment. A successful use of an autonomous mobilerobot depends on its controller. Mobilerobotcontrol is diﬃcult as they are subjected to non-holonomic (non-integrable) kinematic con- straints involving the time derivates of conﬁguration variables  and dynamic constraints. Both analytical like potential ﬁeld method as well as graph-based techniques have been used to solve the navigation problems of robot involving both static and dynamic obstacles. But, all such methods may not be suitable for on-line implementations due to their inherent computational complexity and limitations. Mobilerobot researchers have carried out various researches in this direction using various intelligent techniques methods such as fuzzy logic, neural network and genetic algorithm and their diﬀerent hybrids. Because of the non-linear kinematics of the robot, the uncertainty in sensors readings, and unstructured environmental constrains in the control of mobilerobot navigation; researchers have found fuzzy logic as one of the best intelligent tech- nique for handling the constraints. However, fuzzy logic needs tuning for optimal performance. Hand tuning is very diﬃcult and time consuming therefore there is need for automation of the tuning process. The process of tuning requires learning brought about by training or adaptation of the robot to adapt to its dynamic environment. The poor learning capability of fuzzy logic is compensated for by hybridizing fuzzy logic with other soft computing techniques with excellent learning features such as neural network. In this paper, we present an adaptive neuro-fuzzy controller with genetic algorithm learning for the navigation of Khepera mobilerobot.
Figure 9 demonstrated the mobilerobot mo- vements from position (6.4, 2.5, π/2), the initial dis- tance from the wall is 0.2 m and with t = 20 s. As previous experiments, the fuzzybehavior was obta- ined using FLC, GFC and PSFC. Furthermore Fi- gure 11 (a) presented time response for each algo- rithm, accordingly. Figure 10 and Figure 11 (b) de- monstrated that the three algorithms provided dif- ferent control performance. Fuzzybehavior obtain- ed by FLC had the worst performance. The respon- se time to maintain the distance was very slow. Fur- thermore, the algorithm was also very sensitive to disturbance. It was shown that angular velocities fluctuated and the linear velocity reduced when the mobilerobot runs in the left edge situation. How- ever, the fuzzybehavior obtained by PSFC had bet- ter performance as compared to GFC. For both al- 400
This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobilerobotbased on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobilerobot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobilerobot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobilerobot, a state feedback velocity tracking controller was then proposed for the TWIP mobilerobot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.
Although these methods have been proven to be efficient and provide good short term position estimates, they suffer from unbounded error growth due to the integration of minute measurements to obtain the final estimate . In fact, there is a high demand for mobile robots to use in hard to reach and hazardous areas . This approach involves two steps: first planning a feasible trajectory and second designing a control algorithm to follow this trajectory . This method has a drawback the lack of reactivity. For more reactive navigation, kinematic constraints have been introduced in the spatial representation .
Abstract – A mobilerobot has a capability of sensing its location under uncertain environment, planning a real-time path as well as controlling its steering angle and speed to reach the target location. A robust controller is embedded in mobilerobot whilst analyzing the input and output that help it to navigate without colliding with any obstacles. Meanwhile, Fuzzy Logic Controllers (FLC) is an intelligent technique that proves to be the one of the most reliable controllers that suits well for nonlinear system like robot due to the simple controlbased on user input without any prior knowledge to the mathematical model. In this paper, the Mamdani and Sugeno FLC are developed for a mobilerobot. The smoothness and efficiency that generated from these FLC is analyzed based on simulation of Pioneer P3-DX robot in virtual robotic software for single and multirobot environments under static obstacles environment. Simulation results for the Pioneer P3-DX robot shows the Sugeno FLC able to produce smoother path and reach the goal faster than Mamdani FLC.
In this section a Fuzzy tuning Control (FSC-PID) scheme that uses two fuzzy controllers is used to tune the parameters of the two PID controllers. Each fuzzy controller has two inputs and three outputs used to tune the three parameters (Kp, Ki and Kd),of the PID controller as shown in Figure 2 . This kind of controller is an online controller. Many papers explain the FSC-PID method [18, 19,20] . The fuzzy controller is the master controller which has the ability to tune the PID parameters and PID controller is the slave controller. Figure 2 represents FSC-PID scheme. Each Fuzzy controlle in this scheme has two inputs ( the error and change of error) and three outputs. (𝑠 𝑝 , 𝑠 𝑖 , and 𝑠 𝑑 ) which represent the values
The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain–computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the electroencephalogram (EEG) patterns is based on band power estimates and hidden Markov models (HMMs). The proposed method that combines the EEG patterns based on reparability into subsets of two, three, four, and five mental tasks.
In this paper research has been carried out to develop a navigation technique for an autonomous robot to work in a real world environment, which should be capable of identifying and avoiding obstacles, specifically in a very busy a demanding environment. In this paper better technique is develop in navigating mobilerobot in above mention environment. The action and reaction of the robot is addressed by fuzzy logic control system. The input fuzzy members are turn angle between the robot head and the target, distance of the obstacles present all around the robot (left, right, and front, back).The above mention input members are senses by series of infrared sensors. The presented FLC for navigation of robot has been applied in all complex and adverse environment. The results are hold good for all the above mention conditions.
Brain-controlled mobile robots may be divided into 2 classes per their operational modes. One class is named “direct management by the BCI,” which suggests that the BCI interprets EEG signals into motion commands to regulate robots directly. Numerous approaches to implement this methodology are shown in Fig. I. One typical example is that the work of . United Nations agency 1st developed a brain-controlled robotic chair whose left or right turning movements are directly controlled by corresponding motion commands translated from user brain signals whereas imagining left or right limb movements, and tested this technique in real-world things.
According to the obtained image, selecting a center point A on the vertical center line and representing the actual position of the mobilerobot by the center point. Regarding A as the center, then, scanning left and right sides and determining the width who’s left and right sides of mobile robots by calculating the number of pixels. If the number of pixels on both sides is the same approximately, the mobilerobot will be located at the center line of the path; else, the number of pixels on the left (right) side is more than the right (left) side, the mobilerobot will be located on the right (left) side of the bias path. As a result, it is necessary to control the robot to move to the left (right) side of the path in order to allow it to along the center of the path.
This paper presents a type-2 fuzzy logic control (FLC) approach for mobilerobot tracking a dynamic target in cluttered environments. Robotcontrol actions are generated by di¤erent behaviors : attraction to a dynamic tar- get, obstacle avoidance and fusion block. The proposed controller calcu- lates both the mobilerobot linear and angular velocities from the distance and angle that separate it to the moving target/obstacle. The controller was designed using fuzzy logics theory and then, type-2 fuzzy logic was ap- plied to better accuracy and smoothness of the robot trajectory. Simulation results illustrate that the proposed controller leads to good performances in terms of computational time and tracking errors convergence.
search for signs of alien life, where the spacecraft has to land on the icy surface of Europa, the robot has to move out of the debris, navigate on the completely alien surface of the remote moon, then find a suitable spot to deploy a submersible that has to drill through layers and layers of ancient ice sheets with heat treatment techniques, and look for liquid water beneath the ice. During this process of drilling, the submersible bot has to navigate such that it avoids irregular obstacles in its path and safely makes its way down to the bottom, and if the bot finds liquid water at some depths, then it has to deploy a hydrobot (an aquatic robot) which then has the task of exploring the dark waters of the ancient ocean for even minute signs of life. If any favorable signs are found, then the information has to be relayed back to a base station on Earth. So, such kinds of expensive and highly classified missions require state of the art mobile robots that can safely find their own way out of trouble, return back to the base, repair themselves in case of emergency, store valuable information, require low power supplies etc. All these requirements and the example given above point more and more towards a behavioral model of robotcontrol planning in the days to come.
In the manufacturing line, short traveling distance is sometimes an essential factor to improve productivity. Therefore RM is defined based on the notion that the distance to the target is four times more important than the rotation and six times the safety, and the rotation is twice as important as the safety. Therefore the RM is defined as