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

Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

Design of a Fuzzy Logic Controller for Skid Steer Mobile Robot

The control problem of four-wheeled skid steering mobile robots is quite challenging mainly because the skid steering system is an underactuated system and its mathematical model is highly uncertain. Skid steering configurations employ a differential-drive technique in which the wheels rotation is limited to around one axis and the lack of a steering wheel causes the navigation to be determined by the change of speed in either side of the robot for turning. Equal speed in both sides causes a straight-line motion. However, the implementation of the dead reckoning technique on skid-steer mobile robots will limit the precision of current robot’s position because skid-steer configuration intentionally relies on wheel slippage for normal operation and this possesses some difficulties when implementing motion control using the odometric system. The thesis describes the design of a fuzzy logic controller to compensate the dead reckoning limitation and implementation on a skid-steer mobile robot. The fuzzy controller has two inputs (angle error and distance), two outputs (translational and rotational speed) and 14 rules. These inputs are computed from the dead-reckoning method that is totally reliant on the odometry readings and data are fuzzified to be the inputs of the fuzzy controller. The outputs are the analogue voltages to the left and right motors, which drive the mobile robot. For simplicity, membership functions consisting of triangular and trapezoid shapes have been adopted. The membership functions of the fuzzy sets are chosen by trial-and-error based on experimentation. The heuristic rules control the orientation of the robot according to the information about the distances from the desired positions. The crisp output values from the fuzzy logic controller are decoded and fed into a decision module where the ratios of both sides motor voltage are determined for every smooth change in speed of the motors. To facilitate the implementation of control system, real-time execution is done in an indoor environment. Data acquisition is done in a LABVIEW and a MATLAB control algorithm is called in LABVIEW. A real mobile robot, PUTRABOT2 was used to conduct the experiment. Performance evaluation is observed from the accumulated error in orientation and its trajectory obtained after mapping the information gathered from the real world via odometry sensors. Few features such as the rise time, settling time and peak time of the output responses are analyzed. Comparisons are made between fuzzy logic and PD controllers. Comparative results among these two controllers indicate the superiority of the fuzzy approach with the ability to minimize the position and orientation errors. Moreover, the trajectory accuracy is very high and more reliable in the presence of unreliable odometry readings

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

A layered control architecture for mobile robot navigation

A Thesis submitted to the University Research Degree Committee in fulfillment ofthe requirements for the degree of DOCTOR OF PHILOSOPHY in RoboticsThis thesis addresses the problem of how to control an autonomous mobile robot navigation in indoor environments, in the face of sensor noise, imprecise information, uncertainty and limited response time. The thesis argues that the effective control of autonomous mobile robots can be achieved by organising low level and higher level control activities into a layered architecture. The low level reactive control allows the robot to respond to contingencies quickly. The higher level control allows the robot to make longer term decisions and arranges appropriate sequences for a task execution. The thesis describes the design and implementation of a two layer control architecture, a task template based sequencing layer and a fuzzy behaviour based low level control layer. The sequencing layer works at the pace of the higher level of abstraction, interprets a task plan, mediates and monitors the controlling activities. While the low level performs fast computation in response to dynamic changes in the real world and carries out robust control under uncertainty. The organisation and fusion of fuzzy behaviours are described extensively for the construction of a low level control system. A learning methodology is also developed to systematically learn fuzzy behaviours and the behaviour selection network and therefore solve the difficulties in configuring the low level control layer. A two layer control system has been implemented and used to control a simulated mobile robot performing two tasks in simulated indoor environments. The effectiveness of the layered control and learning methodology is demonstrated through the traces of controlling activities at the two different levels. The results also show a general design methodology that the high level should be used to guide the robot's actions while the low level takes care of detailed control in the face of sensor noise and environment uncertainty in real time

Generation of Evaluation Function for Lige-time Learning of An Intelligent Robot

This paper deals with a mobile robot with structured intelligence. The robot interacts with a dynamic environment. The evaluation criteria or functions are the strategy for the behavior acquisition. Generally, it is difficult for human operators to describe internal models of the robot because the organization of the robot is quite different from that of a human. In the optimization, the evaluation function is generally given by human operators beforehand. It is easy to give the evaluation functions if the environmental condition is easy and fixed. But the robot must interact with dynamic, uncertain and unknown environments or human operators. Therefore, the robot should generate the evaluation criteria by itself based on its embodiment. A human improves its behavior by using and changing its evaluation criteria as adaptive processes. The robot also has to acquire their evaluation criteria through life-time learning. Therefore, we apply genetic programming (GP) for generating evaluation functions. The result of computer simulation shows that GP can generate the evaluation function suitable to the facing environments, the given tasks, and the robot