Design and analysis of Intelligent Navigational controller for Mobile Robot

Since last several years requirement graph for autonomous mobile robots according to its virtual application has always been an upward one. Smother and faster mobile robots navigation with multiple function are the necessity of the day. This research is based on navigation system as well as kinematics model analysis for autonomous mobile robot in known environments. To execute and attain introductory robotic behaviour inside environments(e.g. obstacle avoidance, wall or edge following and target seeking) robot uses method of perception, sensor integration and fusion. With the help of these sensors robot creates its collision free path and analyse an environmental map time to time. Mobile robot navigation in an unfamiliar environment can be successfully studied here using online sensor fusion and integration. Various AI algorithm are used to describe overall procedure of mobilerobot navigation and its path planning problem. To design suitable controller that create collision free path are achieved by the combined study of kinematics analysis of motion as well as an artificial intelligent technique. In fuzzy logic approach, a set of linguistic fuzzy rules are generated for navigation of mobile robot. An expert controller has been developed for the navigation in various condition of environment using these fuzzy rules. Further, type-2 fuzzy is employed to simplify and clarify the developed control algorithm more accurately due to fuzzy logic limitations. In addition, recurrent neural network (RNN) methodology has been analysed for robot navigation. Which helps the model at the time of learning stage. The robustness of controller has been checked on Webots simulation platform. Simulation results and performance of the controller using Webots platform show that, the mobile robot is capable for avoiding obstacles and reaching the termination point in efficient manner

A novel dual surface type-2 fuzzy logic controller for a micro robot

Over the last few years there has been an increasing interest in the area of type-2 fuzzy logic sets and systems in academic and industrial circles. Within robotic research the majority of type-2 fuzzy logic investigations has been centred on large autonomous mobile robots, where resource availability (memory and computing power) is not an issue. These large robots usually have a variation of a Unix operating system on board. This allows the implementation of complex fuzzy logic systems to control the motors. Specifically the implementation of interval and geometric type-2 fuzzy logic controllers is of interest as they are shown to outperform type-1 fuzzy logic controllers in uncertain environments. However when it comes to using micro robots it is not practical to use type-1 and type-2 fuzzy logic controllers, due to the lack of memory and the processor time needed to calculate a control output value. The choice of motor controller is usually either fixed pre-set values, a variable scaled value or a PID controller to generate wheel velocities. In this research novel ways of implementing type-1 and interval type-2 fuzzy logic controllers on micro robots with limited resources are investigated. The solution thatis being proposed is the use of pre-calculated 3D surfaces generated by an off-line Fuzzy Logic System covering the expected ranges of the input and output variables. The surfaces are then loaded into the memory of the micro robots and can be accessed by the motor controller. The aim of the research is to test if there is an advantage of using type-2 fuzzy logic controllers implemented as surfaces over type-1 and PID controllers on a micro robot with limited resources. Control surfaces were generated for both type-1 and average interval type-2 fuzzy logic controllers. Each control surface was then accessed using bilinear interpolation to provide the crisp output value that was used to control the motor. Previously when this method has been used a single surface was employed to hold the information. This thesis presents the novel approach of the dual surface type-2 fuzzy logic controller on micro robots. The lower and upper values that are averaged for the classic interval type-2 controller are generated as surfaces and installed on the micro robots. The advantage is that nuances and features of both the lower and upper surfaces are available to be exploited, rather than being lost due to the averaging process. Having conducted the experiments it is concluded that the best approach to controlling micro robots is to use fuzzy logic controllers over the classical PID controllers where ever possible. When fuzzy controllers are used then type-2 fuzzy controllers (dual or single surface) should be used over type-1 fuzzy controllers when applied as surfaces on micro robots. When a type-2 fuzzy controller is used then the novel dual surface type-2 fuzzy logic controller should be used over the classic average surface. The novel dual surface controller offers a dynamic, weighted, adaptive and superior response over all the other fuzzy controllers examined

A layered fuzzy logic controller for nonholonomic car-like robot

A system for real time navigation of a nonholonomic car-like robot in a dynamic environment consists of two layers is described: a Sugeno-type fuzzy motion planner; and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including right and left views to identify the next step to the goal. A Sugeno-type fuzzy motion planner of four inputs one output is introduced to give a clear direction to the robot controller. The second stage is a modified proportional navigation based fuzzy controller based on the proportional navigation guidance law and able to optimize the robot's behavior in real time, i.e. to avoid stationary and moving obstacles in its local environment obeying kinematics constraints. The system has an intelligent combination of two behaviors to cope with obstacle avoidance as well as approaching a target using a proportional navigation path. The system was simulated and tested on different environments with various obstacle distributions. The simulation reveals that the system gives good results for various simple environments

Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.</jats:p

Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Realization of reactive control for multi purpose mobile agents

Mobile robots are built for different purposes, have different physical size, shape, mechanics and electronics. They are required to work in real-time, realize more than one goal simultaneously, hence to communicate and cooperate with other agents. The approach proposed in this paper for mobile robot control is reactive and has layered structure that supports multi sensor perception. Potential field method is implemented for both obstacle avoidance and goal tracking. However imaginary forces of the obstacles and of the goal point are separately treated, and then resulting behaviors are fused with the help of the geometry. Proposed control is tested on simulations where different scenarios are studied. Results have confirmed the high performance of the method

A reconfigurable hybrid intelligent system for robot navigation

Soft computing has come of age to o er us a wide array of powerful and e cient algorithms that independently matured and in uenced our approach to solving problems in robotics, search and optimisation. The steady progress of technology, however, induced a ux of new real-world applications that demand for more robust and adaptive computational paradigms, tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms and neural networks. As noted in the literature, they are signi cantly more powerful than individual algorithms, and therefore have been the subject of research activities in the past decades. There are problems, however, that have not succumbed to traditional hybridisation approaches, pushing the limits of current intelligent systems design, questioning their solutions of a guarantee of optimality, real-time execution and self-calibration. This work presents an improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search algorithm and the Voronoi diagram generation algorithm

Managing uncertainty in sound based control for an autonomous helicopter

In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper ). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system