Navigation Control Of Mobile Robot Research Articles

MATLAB Simulation for Mobile Robot Navigation with Hurdles in Cluttered Environment Using Minimum Rule based Fuzzy Logic Controller

In this paper concentrated on the design of a minimum rule based fuzzy-logic controller for robot navigation, and hurdles avoidance in cluttered environment, based on the Mamdani type fuzzy method. The controller has 3 inputs, and single output. This technique generates suitable heading angle maneuvers control of the autonomous vehicle which is used by the robot to reach its goal safely without any collision in cluttered environment. Simulation results show the method can be used for wheeled mobile robot moving on in cluttered environment with lot of hurdles. We present a path-planning system that can control and safely navigate robot motion in a static environment. The success of the mobile robot navigation control depends mostly on the accuracy of absolute measurements of its position, hurdle distances, goal distance, velocity, orientation, and its rate of change its heading angle. The whole navigation system has been tested in a simulation environment with satisfying results.

Behavior-Based Fuzzy Control for Mobile Robot Navigation

A new behavior-based fuzzy control method for mobile robot navigation is presented. It is based on behavioral architecture which can deal with uncertainties in unknown environments and has the ability to accommodate different behaviors. Basic behaviors are controlled by specific fuzzy logic controllers, respectively. The proposed approach qualifies for driving a robot to reach a target while avoiding obstacles in the environment. Simulation and experiments are performed to verify the correctness and feasibility of the proposed method.

Adaptive Wavelet Fuzzy Control for Mobile Robot Navigation in Dynamic Environment

Adaptive Wavelet Fuzzy Control for Mobile Robot Navigation in Dynamic Environment

Robot Navigation Based on Discrimination of Artificial Fields: Application to Robot Formations

In the preceding paper, a method for mobile robot navigation control based on discrimination of multiple artificial fields was introduced. In this second paper, the method is extended to robot formations. Experimental demonstrations are presented taking examples of four types of formations. The experiments cover formation initialization, maneuvering, obstacle avoidance and formation switching.

Integrating planning and control for single-bodied wheeled mobile robots

This paper presents an approach to couple path planning and control for mobile robot navigation in a hybrid control framework. We build upon an existing hybrid control approach called sequential composition, in which a set of feedback control policies are prescribed on well-defined domains contained in the robot's free space. Each control policy drives the robot to a goal set, which lies in the domain of a subsequent policy. Control policies are deployed into the free state space so that when composed among one another, the overall action of the set of control policies drives the robot to perform a task, such as moving from a start to a goal location or patrolling a perimeter. A planner determines the sequence of control policies to be invoked. When control policies defined in this framework respect the low-level dynamics and kinematics of the system, this formal approach guarantees that high-level tasks are either accomplished by a given set of policies, or verifies that the tasks are not achievable with the given policies. Specifically, this paper makes three key contributions to the technique of sequential composition. First, we allow for a larger variety of transitions among the control policies, which results in a richer set of generated behaviors. In particular, we are able to design policies that respect multiple interacting constraints including obstacles, nonholonomic constraints, and input bounds. This is achieved by utilizing flow-through control policies, which allow the robot to exit the policy domain through a subset of the domain boundary. Second, our hybrid control approach lifts the planning problem from working in trajectory space to control policy space, allowing for both more efficient and meaningful symbolic planners to be employed for systems with nonholonomic constraints. Finally, we address the issue of deploying control policies in configuration spaces whose obstacles have curvature (i.e., are not polytopes in configuration space); ultimately, such an approach results in the collection of policy domains only approximating the free space. We believe this trade-off is necessary, and provide a technique to measure the relative completeness of the approximation. The paper concludes with an experimental validation of our approach on a mobile robot.

Evolvable Neural Networks Based on Developmental Models for Mobile Robot Navigation

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

Takagi-Sugeno Based Controller for Mobile Robot Navigation

Takagi-Sugeno Based Controller for Mobile Robot Navigation

Navigation of Mobile Robots Using a Fuzzy Logic Controller

This paper describes a mobile robot navigation control system based on fuzzy logic. Fuzzy rules embedded in the controller of a mobile robot enable it to avoid obstacles in a cluttered environment that includes other mobile robots. So that the robots do not collide against one another, each robot also incorporates a set of collision prevention rules implemented as a Petri Net model within its controller. The navigation control system has been tested in simulation and on actual mobile robots. The paper presents the results of the tests to demonstrate that the system enables multiple robots to roam freely searching for and successfully finding targets in an unknown environment containing obstacles without hitting the obstacles or one another.

Behavior-based neuro-fuzzy controller for mobile robot navigation

This paper discusses a neuro-fuzzy controller for sensor-based mobile robot navigation in indoor environments. The control system consists of a hierarchy of robot behaviors.

A neuro-fuzzy controller for mobile robot navigation and multirobot convoying

A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets.

Fuzzy knowledge-based controller design for autonomous robot navigation

In this study a fuzzy logic controller for mobile robot navigation has been designed. The designed controller deals with the uncertainty and ambiguity of the information the system receives. The technique has been used on an experimental mobile robot which uses a set of seven ultrasonic sensors to perceive the environment. The designed fuzzy controller maps the input space (information coming from ultrasonic sensors) to a safe collision-avoidance trajectory (output space) in real time. This is accomplished by an inference process based on rules (a list of IF-THEN statements) taken from a knowledge base. The technique generates satisfactory direction and velocity maneuvers of the autonomous vehicle which are used by the robot to reach its goal safely. Simulation and experimental results show the method can be used satisfactorily by wheeled mobile robots moving on unknown static terrains.

Neural Predictive Control for Mobile Robot Navigation in a Partially Structured Static Environment

This paper presents a way of implementing a Model Based Predictive Controller (MBPC) for mobile robot navigation when unexpected static obstacles are present in the robot environment. The method uses a non-linear model of mobile robot kinematics and thus allows an accurate prediction of the future trajectories. An ultrasonic ranging system has been used for obstacle detection. A Multilayer Perceptron is used to implement the MBPC, allowing real-time and also eliminating the need for data sensor high level processing. The perceptron has been trained to reproduce the MBPC behaviour in a supervised manner. Experimented results obtained when applying the neural network controller to a mobile robot are given in the paper.

A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation

Payton and Rosenblatt (1990) have proposed a command fusion method for combining outputs of multiple behaviors in a mobile robot navigation system such that information loss due to command fusion can be reduced. Using linguistic fuzzy rules to explicitly capture heuristics implicit in the Payton-Rosenblatt approach, we have extended their approach to a fuzzy logic architecture for mobile robot navigation in dynamic environments, which is simpler and easier to understand and modify. We have also developed and empirically tested a new defuzzification technique for alleviating difficulties in applying existing defuzzification methods to mobile robot navigation control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>