Kemal Kaplan

Market-Driven Multi-Agent Collaboration in Robot Soccer Domain

In recent robotic studies, in many key areas decoupled multi-agent systems have become more popular than complex single agent systems, where the former is more robust, fast and cheap to implement. The most important reason behind this preference is to eliminate the possibility of single point of failure, which is a vital concern for single complex agents. Usage of decoupled multi-agent systems may also reduce the total cost of the entire system when it is possible to use a team of single cheap robots for performing complex tasks, instead of building up a single complex and expensive robot to satisfy all the system needs. As a result, typically when a team of robots is used, the system throughput increases while the total cost decreases. Since the robots usually have simpler physical structures, generally less complicated controller programs are necessary to manipulate the agents. The decoupled behaviors of agents can cause communication and coordination problems, however. The studies in (Dudek

All Bids for One and One Does for All: Market-Driven Multi-agent Collaboration in Robot Soccer Domain

In this paper, a novel market-driven collaborative task allocation algorithm called “Collaboration by competition / cooperation” for the robot soccer domain is proposed and implemented. In robot soccer, two teams of robots compete with each other to win the match. For the benefit of the team, the robots should work collaboratively, whenever possible. The market-driven approach applies the basic properties of free market economy to a team of robots for increasing the profit of the team as much as possible. The experimental results show that the approach is robust and flexible and the developed team is more succcessful than its opponents.

Practical extensions to vision-based monte carlo localization methods for robot soccer domain

This paper proposes a set of practical extensions to the vision-based Monte Carlo localization (MCL) for RoboCup Sony AIBO legged robot soccer domain. The main disadvantage of AIBO robots is that they have a narrow field of view so the number of landmarks seen in one frame is usually not enough for geometric calculation. MCL methods have been shown to be accurate and robust in legged robot soccer domain but there are some practical issues that should be handled in order to maintain stability/elasticity ratio in a reasonable level. In this work, we presented four practical extensions in which two of them are novel approaches and the remaining ones are different from the previous implementations.

ADES: Automatic Driver Evaluation System

Most of the traffic accidents occurred in the world and in our country are caused by the drivers. ADES (Automatic Driver Evaluation System) project targets to present a framework for integrating different applications for driver evaluation purpose. The proposed system can be divided into two main modules. The first one, which is the data acquisition and processing module, acquires the sensor information from the outside world and process this data for presenting valuable information to the decision system. The system may benefit from standard vehicle sensors like cameras or GPS (Global Positioning System) systems as well as non standard devices like RFID (Radio Frequency Identification) readers. The second module is the inference engine, which processes the information provided by the first module and makes judgments about the actions of the driver. A sample implementation of the proposed framework is also tested in the simulation environment and by using real video recordings.

Fast lane tracking for autonomous urban driving using hidden Markov models and multiresolution Hough transform

Purpose – Lane tracking is one of the most important processes for autonomous vehicles because the navigable region usually stands between the lanes, especially in urban environments. A robust lane tracking method is also required for reducing the effect of the noise and the required processing time. The purpose of this paper is to present a new lane tracking method.Design/methodology/approach – A new lane tracking method is presented which uses a partitioning technique for obtaining multiresolution Hough transform of the acquired vision data where Hough transform is one of the most popular algorithms for lane detection. After the detection process, for tracking the detected lanes, a hidden Markov model (HMM) based method is proposed.Findings – The results of the proposed approach show that the partitioned Hough transformation reduces the effect of noise and provides robust lane tracking. In addition, the acquired lanes are successfully tracked by using the designed HMM.Originality/value – This paper prov...

Real-time traffic sign detection and classification method for intelligent vehicles

In this paper, a real-time traffic sign detection and classification system with several contributions to improve the different phases of sign detection and classification is introduced. This work is a part of Automatic Driver Evaluation System (ADES) Project. The proposed system uses affine transformation coefficients as genetic algorithm parameters for sign detection. For the classification phase, the results show that neural networks are better than support vector machines for this specific application. The processing times and error rates show that this system can be used as a part of complex real-time applications.

Expert system design for an Autonomous Driver Evaluation System

In this paper, two expert system implementations for the Automatic Driver Evaluation System (ADES) Project are introduced. These expert systems are used for deciding whether a driver violates the traffic rules or not, according to the facts derived from the data acquired by the sensors. Sample scenarios are executed in a highly realistic simulation environment. The advantages and shortcomings of both approaches are discussed.