Cem Ünsal

Sliding mode measurement feedback control for antilock braking systems

We describe a nonlinear observer-based design for control of vehicle traction that is important in providing safety and obtaining desired longitudinal vehicle motion. First, a robust sliding mode controller is designed to maintain the wheel slip at any given value. Simulations show that longitudinal traction controller is capable of controlling the vehicle with parameter deviations and disturbances. The direct state feedback is then replaced with nonlinear observers to estimate the vehicle velocity from the output of the system (i.e., wheel velocity). The nonlinear model of the system is shown locally observable. The effects and drawbacks of the extended Kalman filters and sliding observers are shown via simulations. The sliding observer is found promising while the extended Kalman filter is unsatisfactory due to unpredictable changes in the road conditions.

A Modular Self-Reconfigurable Bipartite Robotic System: Implementation and Motion Planning

In this manuscript, we discuss I-Cubes, a class of modular robotic system thatis capable of reconfiguring itself in 3-D to adapt to its environment. This is abipartite system, i.e., a collection of (i) active elements for actuation, and (ii)passive elements acting as connectors. Active elements (links) are 3-DOFmanipulators that are capable of attaching/detaching from/to the passive elements(cubes), which can be positioned and oriented using links. Self-reconfigurationcapability enables the system to perform locomotion tasks over difficult terrain; theshape and size can be changed according to the task. This paper describes the designof the system, and 3-D reconfiguration properties. Specifics of the hardwareimplementation, results of the experiments with the current prototypes, our approachto motion planning and problems related to 3-D motion planning are given.

Mechatronic design of a modular self-reconfiguring robotic system

Design and implementation of I-Cubes, a modular self-reconfigurable robotic system, is discussed. I-Cubes is a bipartite collection of individual modules that can be independently controlled. The group consists of active elements, called links, which are 3-DOF manipulators capable of attaching to/detaching from the passive elements (cubes) acting as connectors. The cubes can be oriented and positioned by the links. Using actuation and attachment properties of the link and the cubes, the system can self-reconfigure to adapt to its environment. Tasks such as moving over obstacles, climbing stairs can be performed by changing the relative position and connection of the modules. The links are actuated using servomotors and worm gear mechanisms. Mechanical encoders and rotary switches provide position feedback for semi-autonomous control of the system. The cubes are equipped with a novel mechanism that provides inter-module attachment. Design and hardware implementation of the system as well as experimental results are presented.

Multiple stochastic learning automata for vehicle path control in an automated highway system

This paper suggests an intelligent controller for an automated vehicle planning its own trajectory based on sensor and communication data. The intelligent controller is designed using the learning stochastic automata theory. Using the data received from on-board sensors, two automata (one for lateral actions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments, unlike adaptive control methods or expert systems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging results.

A hierarchical motion planning strategy for a uniform self-reconfigurable modular robotic system

Describes a multi-layered hierarchical motion planning strategy for a class of self-reconfigurable modular robotic systems, I-Cubes. The approach is based on the synthesis of motion on the basis of metacubes, which have a particular structure possessing 8 Cubes and 16 Links. The developed strategy organizes the metacube motions and the corresponding cube-level motions. At the lowest level, link motions are generated. The resulting system is demonstrated to be capable of performing a pre-specified task of moving from one position/shape to another. The paper describes the latest results of our planning strategy through some experimentally justified examples.

A multi-layered planner for self-reconfiguration of a uniform group of I-Cube modules

In this paper, we present a multilayered planner for the motion of modules in a uniform group of I-Cube self-reconfiguring modular robotic system. The planner uses metacubes, the high-level abstraction of 8C16L groups. It combines distributed approaches at the high-level with lowlevel trajectory computation for the actual modules which can be completed in O(n) steps where n is the number of the cubes in the system, and pre-defined rules for link motions. Mechatronic properties of the latest version of the reconfiguring modules are presented as well as results of the latest hardware and software experiments with the I-Cube modules.

I(CES)-cubes : a modular self-reconfigurable bipartite robotic system

In this manuscript, we introduce I(CES)-Cubes, a class of 3D modular robotic system that is capable of reconfiguring itself in order to adapt to its environment. This is a bipartite system, i.e. a collection of (i) active elements capable of actuation, and (ii) passive elements acting as connectors between actuated elements. Active elements, called links, are 3-DOF manipulators that are capable of attaching/detaching themselves to/from the passive elements. The cubes can then be positioned and oriented using links, which are independent mechatronic elements. Self- reconfiguration property enables the system to performed locomotion tasks over difficult terrain. For example, the system would be capable of moving over obstacles and climbing stairs. These task are performed by positing and orienting cubes and links to form a 3D network with required shape and position. This paper describes the design of the passive and active elements, the attachment mechanics, and several reconfiguration scenarios. Specifics of the hardware implementation and result of experiments with current prototypes are also given.

Motion Planning for a Modular Self-Reconfiguring Robotic System

In this paper, we address the issue of motion planning for a bipartite class of modular self-reconfiguring robotic system (I-Cubes) that is a collection of active elements providing reconfiguration (3-DOF manipulators called links) and passive elements acting as connectors (cubes). The links, capable of attaching/detaching themselves from/to cubes, can position and orient the cubes.

Simulation study of learning automata games in automated highway systems

We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible actions to avoid collisions. Although the learning approach taken is capable of providing a safe decision, optimization of the overall traffic flow is required. This can be achieved by studying the interaction of the vehicles. The design of the adaptive vehicle path planner based on local information is extended with additional decision structures by analyzing the situations of conflicting desired vehicle paths. The analysis of the situations and the design of these structures are made possible by treatment of the interacting reward-penalty mechanisms in individual vehicles as automata games.

Intelligent control of vehicles: preliminary results on the application of learning automata techniques to automated highway system

We suggest an intelligent controller for an automated vehicle to plan its own trajectory based on sensor and communication data received. Our intelligent controller is based on an artificial intelligence technique called learning stochastic automata. The automaton can learn the best possible action to avoid collisions using the data received from on-board sensors. The system has the advantage of being able to work in unmodeled stochastic environments. Simulations for the lateral control of a vehicle using this AI method provides encouraging results.