Elmer P. Dadios

Cooperative mobile robots with obstacle and collision avoidance using fuzzy logic

This paper presents a fuzzy controller technique in navigation with obstacle and collision avoidance for cooperative micro robots playing soccer. The dynamic positions of the robots and obstacles are taken into consideration. The information needed about the robot environment is the destination point, the distance between the robots, and obstacles and is taken from a camera vision system. The obstacles can be the opponent robots, the teammate robots, and the soccer field boundaries. Fuzzy logic is used to control the navigation of a robot as it avoids the presence of an obstacle along its way to the destination point. Obstacle and collision avoidance can be achieved by changing the direction angle of the robot. The cooperative behavior of the robots is tested based on team performance in playing the soccer game. The effectiveness of the technique developed is shown in the results of real time experiments. The fuzzy logic controller developed proves to be more accurate and robust compared to other conventional controllers.

Multiple fuzzy logic systems: a controller for the flexible pole-cart balancing problem

This paper examines the applicability of fuzzy logic based algorithms to the control of complex and highly nonlinear systems. An online fuzzy logic controller has been developed to control a cart balancing a flexible pole under its first mode of vibration. The controller design includes 5 fuzzy logic systems (FLS) and a single rule based evaluator that centers the pole on the truck. Results of physical experiments show that the controller not only balances the flexible pole indefinitely but also brings the cart to the center of the track. The controller can also easily adapt to disturbances from the external environment (e.g. moving or shaking the track randomly, elevating the height of the track on either side, pushing the pole in any direction, preventing the pole from moving further by putting an obstacle in its path). The operation of the system can also be initialized anywhere in the track. The controller is sufficiently fast to balance the system from an initial angle of 20 degrees.

Vehicle parking inventory system utilizing image recognition through artificial neural networks

An automated vehicle logging system is introduced in this paper. The system utilizes character recognition through images captured from the entrance of a parking area. These images are processed to extract the licensed plates of any vehicle entering the parking area. Extracted plates images are then converted into numerical forms devised by researchers to fit the requirements of the artificial neural network. From the numbered plate, each character is then extracted to produce their distinct features. Character recognition engine is primarily implemented using feed forward neural networks. There are 50 input neurons which are defined by resizing each character into 25×25 pixel image and summing all the pixel values in each row and each columns resulting to 50 sums. After which a numerical value will be produce and will signify a character equivalent. Characters are recognized separately. This process is done until all of the characters are recognized. Afterwards, these characters are then concatenated to produce the plate number identity. The system is trained using 5860 sets of training data yielding a system with 0.0001645724% error.

Nonconventional control of the flexible pole-cart balancing problem: Experimental results

Emerging techniques of intelligent or learning control seem attractive for applications in manufacturing and robotics. It is however important to understand the capabilities of such control systems. In the past the inverted pendulum has been used as a test case, however, this problem is not sufficiently testing. This research therefore concentrates on the control of the inverted pendulum with additional degrees of freedom as a testing demonstrator problem for learning control system experimentation. A flexible pole is used in place of a rigid one. The transverse displacement of the flexible pole has distributed elasticity and therefore infinite degrees of freedom. The dynamics of this new system are more complex as the system needs additional parameters to be defined due to the poles elastic deflection. This problem also has many of the significant features associated with flexible robots with lightweight links as applied in manufacturing. Novel neural network and fuzzy control systems are presented that control such a system in real time in one of its modes of vibration. A fuzzy-genetic approach is also demonstrated that allows the creation of fuzzy control systems without the use of extensive knowledge.

A fuzzy-genetic controller for the flexible pole-cart balancing problem

This paper investigates the applicability of developing a controller based on genetic algorithms combined with fuzzy logic to control the flexible pole-cart balancing problem. The genetic algorithm is used to obtain the values of the variables required by the fuzzy logic controller, removing the need for expert knowledge. The system employs genetic search to extract the fuzzy rules and membership functions using an objective function calculated from the fuzzy logic system evaluation function. The extracted rules are used in the fuzzy associative memory matrix entries so that the fuzzy logic system performance fits the desired behaviour. Results show that the controller developed is able to predict the desired output for the flexible pole-cart balancing problem with high accuracy.

Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors : Swarm Behavior for Aggregation, Foraging, Formation, and Tracking

This paper presents the fusion of swarm behavior inmulti robotic system specifically the quadrotors un-manned aerial vehicle (QUAV) operations. This studydirected on using robot swarms because of its key fea-ture of decentralized processing amongst its member.This characteristicleadstoadvantagesofrobotopera-tionsbecauseanindividual robotfailurewill notaffectthe group performance. The algorithm emulating theanimal or insect swarm behaviors is presented in thispaper and implemented into anartificial roboticagent(QUAV) in computer simulations. The simulation re-sults concluded that for increasing number of QUAVthe aggregation accuracy increases with an accuracyof 90.62%. The experiment for foraging revealed thatthe number of QUAV does not affect the accuracy ofthe swarm instead the iterations needed are greatlyimproved with an averageof 160.53iterations from 50to 500 QUAV. For swarm tracking, the average accu-racy is 89.23%. The accuracy of the swarm forma-tion is 84.65%. These results clearly defined that theswarmsystemis accurateenoughto performthe tasksand robust in any QUAV number.Keywords: swarm robotics, swarm intelligence, socialbehaviors, unmannedaerial vehicles

Machine vision for traffic violation detection system through genetic algorithm

This paper presents a machine vision algorithm to detect traffic violations specifically swerving and blocking the pedestrian lane. The proposed solution consists of background difference method, and focuses on the genetic algorithm of the system to detect these violations. The general process is as follows: a capture picture is to be subtracted first by the reference image, then the genetic algorithm is run to find the violator, and finally a display is outputted with the corresponding type of violation. The machine vision traffic violation detection system was found to have an average convergence of about 8 iterations, within an average of less than 300 generations. These results show that the algorithm is well-suited for real time implementation in traffic detection system. Provided the system inputs were captured photos from a CCTV camera, whereas the outputs were cropped pictures of the car that was detected to have such violations mentioned earlier.

Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm

There is an increasing research interest in unmanned autonomous vehicles (UAVs) such as quadrotors. These researches applies these quadrotors for much more complicated tasks with most requiring cameras and GPS modules for positioning. This paper presents an alternative way of position localization of a quadrotor without the use of cameras and GPS modules by means of transceivers and Genetic Algorithm (GA). This paper uses the received signals from the transceivers as inputs for the genetic algorithm in order to locate the quadrotor in a xyz axis. Parameters such as location of transceivers, amount of transceivers and population size of the GA are tested in order to determine a successful way of locating the quadrotor. Results show that the different parameters tested were successful and converges to a point usually with a fitness measure greater than 99%. An average fitness measure greater than 99.9900% served as a benchmark for the tests done. The first test achieved this benchmark at about 130 generations and the second test achieved it at 110 generations. The time it took for the program to locate the quadrotor is about 60 milliseconds. Results show that this blind localization technique is successfully locates the quadrotor and may be calibrated to ones own need.

A genetic algorithm and artificial neural network-based approach for the machine vision of plate segmentation and character recognition

This paper proposes a genetic-algorithm and neural network-based approach in the optimization of the process of plate segmentation and character recognition respectively in intelligent transportation systems. Upon the detection of the vehicles plate from a captured image, it is necessary that the individual characters in the detected plate are distinguished. After the process of plate recognition, the recognized plate number can be crossed-referenced against a database to correctly identify the vehicles owner and ultimately penalize him for the traffic rule he violated. The segmentation algorithm captures the region of each character in the detected plate using genetic algorithm. After which, each plate character image is mapped against its corresponding sample character image. This is done by feeding sample character images into an artificial neural network and training the network.

Smart farm using wireless sensor network for data acquisition and power control distribution

Smart Farming makes a tremendous contribution for food sustainability for 21st century. Using wireless sensor network in farming from; independent power source distribution, monitoring valves and switches operation, and remote area control will efficiently produce excellent quality farm products in all season. In order to control farm power distribution and irrigation system, this paper proposes a communication methodology of the wireless sensor network for collecting environment data and sending control command to turn on/off irrigation system and manipulate power distribution. The simulation results shows that the proposed system developed is accurate robust and reliable.