In this paper we design and implement a 2D coupled mobility snake/caterpillar vehicle for terrains with variable friction. A control hierarchy made of fuzzy logic controllers equipped with fuzzy actor critic learning (FACL) enables the robot to adapt itself to variable friction conditions of unknown, unstructured environments as well as the need for task changes in those environments such pushing aside a debris for its removal from the navigation site, during locomotion.

The paper presents a fluid dynamics based framework for control of emergent behaviors of robot swarms that are modeled as fluids. A distributed low-level control mechanism is developed based on Smoothed Particle Hydrodynamics (SPH) and it is coupled with a high-level control layer that is responsible for the control of fluid parameters to generate desired behaviors from the swarming characteristics of the robots. It is shown by simulations that using the same low-level SPH model, different swarming behaviors can emerge from the local interactions of robots according to the settings of the fluid parameters that are controlled by the high-level control layer.

This work focuses on the design and implementation of a behavior based, layered, cooperative control of robots in a sensor network, hybrid in combining subsumption and motor schema layers. Behaviors at different level of resolution are not only coordinated by the motor schema layers but also through units called "evaluators" which fuses state and activity of behaviors according to priorities and cross-couplings in runtime. Concurrent, sequential, asynchronous multi-robot task allocation is based on "market based auction algorithm" with a novel efficient non-linear time varying fitness calculation enhancing fairness and fault tolerance. A 3D simulation environment is developed to test the proposed architecture for fairness, fault tolerance, mission accomplishment rates, and coverage

In this paper, we develop a distributed intelligent fuzzy learning controller for serpentine locomotion while doing lasso-type grasping in 3D environments. The novelty of our approach is not only in the control modularity but on a lasso type grasping having as a moving base the remaining snake links undergoing serpentine gait. Dynamical constraints due to environmental conditions are considered online by a genetic algorithm based evaluator, which evaluates the feasibility of behaviors suggested by the high level controller and modify them. Our approach is demonstrated on the simulation of our modular 12 link snake robot

We propose a novel approach inspired by compressible fluids as a distributed and scalable solution to the deployment problem of mobile sensor networks in unknown environments. Our approach is based on fluid dynamics through which we model the sensor network as a fluid body and each sensor node as a fluid element. Originating from local neighborhood interactions, the principles of fluid flow are adapted to the deployment of the sensor network. Mimicking the diffusive and self-spreading behavior of compressible fluids in a mobile sensor network, we achieve the desirable properties of effective coverage, scalability, and distributed self-deployment. An adaptive deployment scheme is also devised for urban disaster environments where the number of nodes to be deployed may not be determined a priori. Simulation results show that our approach can guarantee a desired level coverability

In this paper, a distributed intelligent fuzzy learning controller architecture is developed for a snake robot to avoid obstacles while reaching a target, in a dynamic environment. Moreover, in order to use the robot in object carrying search and rescue (SAR) applications, a grasping scheme of desired objects is added to the abilities of the controller. Behavior-based approach is also incorporated by "target reaching", "link following", "obstacle avoidance" and "object grasping" behaviors. Each link of the robot is separately controlled and able to select a behavior at any time. Behaviors are all realized as fuzzy actor critic learning (FACL) controllers (Jouffe, 1998). In order to move the robot in between different configurations and take dynamics into account, an online optimization technique based on genetic algorithms is employed.

In this paper a gait synthesizer for a hexapod robot working in search and rescue operations is designed. The gait synthesizer is composed of three modules responsible for selecting a new gait, evaluating tide current one, and modifying the recommended gait according to the internal reinforcements of past time steps. A fuzzy logic controller is implemented in selecting the new gaits. Different hexapod wave gaits are encoded within its fuzzy rules to model the gaits in the rule base of the controller. Simulation results demonstrate the potential of the gait synthesizer for search and rescue operations such as its adaptability to the uneven terrain by shaping gaits, getting out of an entrapment of a leg, modifying gaits when some legs are used as manipulators.

Our aim in this work is to generate an intelligent interface for profiling and evaluating each student remotely accessing a robot assisted lab setup. The interface provides the student with the "hands on" experience by necessary visual feedback, giving this user as much freedom as possible to control the experiment. Besides, the system evaluates the user's performance, adapts the context to the level of user's acquired knowledge and skill, and thus intelligently coaches the user to successfully do the experiment. The system's ability is demonstrated on an example and the behavior of each module in the proposed system is discussed. The major contribution of this paper is taking the student behavior into consideration while providing online robotics experimentation, evaluating the user's performance and coaching him/her towards the successful achievement of the tasks.

We propose in this paper a method to enhance the performance of a coupled global positioning/inertial navigation system (GPS/INS) for both air and land navigation applications during the GPS signal loss. Our method is based on using an artificial neural network (ANN) to intelligently aid the GPS/INS coupled navigation system in the absence of GPS signals. The proposed enhanced GPS/INS is used in the very dynamic environment of a tour of an autonomous van on the METU campus and we provide the results here. Our GPS/INS+ANN system performance is thus demonstrated with the land trials.

Develops a new gait synthesizer for a snake-like robot in an unstructured changing environment similar to earthquake rubbles. The gait synthesizer, inspired from the GARIC Architecture, is implemented on a simulated navigation of a snake robot. We consider 6 prototypical gaits classified into vertical undulation, lateral undulation, heading changing right/left, flapping right/left. The gait synthesizer is composed of a gait selection network that decides on a combination of prototypical gaits based on a fuzzy controller which is further tuned by a gait evaluation module based on an ANN that learns from prior performances of robot locomotion and acts as an arbiter unit on gait performance. The third module is a stochastic gait modifier, which takes feedback information from the dynamic robotic system and changes the gait based on reinforcement learning.

This paper presents a logical sensor to be used in the intelligent perception for the motion planning of a mechanical snake searching for rescue in an unstructured environment of collapsed buildings after a natural disaster. The multi-link robot is equipped with ultrasound sensors and a thermal sensor, which detects body heat and isolates any survivor. In this study, the objective is to develop a logical sensing and an intelligent perception model that learns to identify the most suitable zone for control by minimizing the control unpredictability due to uncertainty. This model is based on a radial basis function network trained with dynamic decay adjustment algorithm that classifies the control region into positive regions. Uncertainty irregularities in the control region is modeled using the statistical rough set theory based on beliefs assigned to control cells in a cellular space. Logical sensors are then fused together to construct a wide-angle logical sensor unit in the control zone. Simulation results are given, in order to illustrate the efficiency of the approach developed for an unstructured environment.

We introduce an online transition detection method of degradation of control predictability to robotic systems which can behave chaotically depending on the variations of the control parameter. This method allows us to control such systems at the boundary of the chaotic region until the onset of degradation thus allowing us to use the whole richness of information for control. In our approach, a safe control parameter interval is chosen initially where the system is certainly nonchaotic and the system behavior is initiated. The maximum Lyapunov exponents corresponding to each parameter value are estimated online. The parameter interval is then widened according to the estimated exponents until the maximum exponent approaches a small negative value. At this value, the estimation is ended and the controller begins to work in this maximum possible controller parameter interval. This method allows the designer to have a more efficient controller and a more agile system. This approach is applied to a robot arm under dynamic load.

This paper presents the multisensor-based online path planning of a serpentine robot in the unstructured changing environment of earthquake rubble during search for survivors. The robot has 6 links and 16 controllable degrees of freedom and is equipped with an ultrasound sensor and a camera on its head and infrared sensors along the links of the robot for obstacle avoidance. The serpentine robot locomotion is achieved as a combination of basic snakelike and wormlike gaits which are that of lateral undulation, rectilinear motion, flapping motion (right, left), and of torsional rotation (which has the pivot point at the center of the robot). The path planning algorithm is adopted from distance transform based exploratory path planning and modified to our serpentine robotic configuration. The adopted method is used for calculating the cost of the possible next configuration states so that the snake/worm like robot selects its next gait accordingly. Environmental data is obtained from the multiple sensors located along the body of the robot. The sensors are located in the following configuration: one ultrasound sensor for detecting the obstacles in front of the robot and constructing the map for free space, and 12 infrared sensors (6 pairs) which are located on the left and right of the joints of the robot. Simulation results are presented in the paper as an illustration of the efficiency of the method in complex unstructured environments.

We introduce in this paper a new fractal/rough set modeling approach to the domains of attraction of nonlinear systems obtained by cell mapping. The state space is partitioned into cells and the stability regions found using cell to cell mapping. Our new approach gives a fractal rough set identity to the domains of attraction where cells are identified according to their fractal dimension as fully stable, possibly stable and unstable. There the stability domain is a rough set where fully stable cells determine the lower approximation of the domain, and possibly stable cells its rough boundary. Consequently, the totality of these cells forms an upper approximation to the rough stability domain. The boundary of this domain which is a rough set of cells having a fractal dimension as an attribute of roughness is smoothed, minimising the inherent stability uncertainty of the region, using a reinforcement learning technique which takes into account the stability history of each fractal/rough cell. This new approach intended to reinforce the performance of a controller under stability uncertainty is applied for illustrative purposes to a two-axis robot arm under dynamic load.

This paper introduces a methodology developed for the self-localization of a holon, within the reconfiguration task space of its colony under a highly decentralized decision-making and colony reconfiguration control. Holons act as intelligent agents of a colony in this decentralized structure. We term such an n-holon colony where intelligent agents can self-localize in reconfiguration task space without their tasks within a configuration being assigned by a central controller, as an n-holony. Our approach begins the self localization in task domain of a holon by proposing a way of extracting the invariants of the global state of the holony configuration which will then be used to infer the localization and action estimation by a holon using local knowledge. The holon communicates with its neighbours in order to modify its local knowledge towards a global one. This is the basic inference structure of a holony that is novel when compared to that of a colony of holons.

This paper uses a holonic hand model introduced in our early works (1998), which is composed of 2-stems-of-links-1-joint nonconvex biped planar holons working in colony in order to perform dexterous grasps. This paper focuses on the reconfiguration control of the colony that we structure as a sequence of planar grasp operations defining a holonic regrasp, which uses a graph theoretic formalization of configuration changes that occur in this holonic hand during this regrasping phase.

This paper introduces a methodology developed for the self-organization of a colony of holons in its task space where holons act as intelligent agents in this decentralized robotic structure such that they self localize themselves in the reconfiguration task space of their colony and identify their individual role in the colony re-organization. We term such an n-holon colony where intelligent agents can self-localize in the reconfiguration task space without their individual tasks within the colony configuration being assigned by a central controller, an n-holony. The proposed method is based on a cascaded computation scheme for the self-reconfiguration of the n-holony: self-localization followed by self-organization. The holon communicates with its neighbours in order to acquire knowledge about the colony configuration within a neighbourhood of radius d based on configuration invariants namely the eigenstructures. Self-organization uses this knowledge to take the next action for achieving the desired holony configuration.

We develop a 2-way adaptive fuzzy control system that makes use of the intuitionistic fuzzy sets for modeling expert knowledge bearing uncertainty. Adaptive fuzzy control systems are fuzzy logic systems whose rule parameters are automatically adjusted through training. In our system, all supports to propositions have interval valued distributions with necessity at the lower bound and possibility at the upper. Uncertainty in expert knowledge determines the width of the interval. Our first level training tunes rule parameters with necessity function values, while the second level training re-adjusts these parameters so as to minimize uncertainty based on possibility function values. The intuitionistic 2-way adaptive fuzzy controller is found to have a better performance due to the richness of information in interval valued supports with nonantagonistic bounds opposed to antagonistic ones in ordinary fuzzy sets.

This paper investigates the holonic modeling of multifingered robotic hands where 2-stem-of-links+1-joint holons having basic kinematic properties are considered, working in colony in order to perform dexterous grasps. Those holons are the simplest physical elements, which have the capabilities of independent actuation, and of cooperation with other holons in achieving global behavior for the whole structure. Our work focuses on the concept of self-reconfigurability of the modular colony structure so as the holons reconfigure themselves to a pre-described structure in the absence of a human in the loop. The reconfiguration control of the dynamical holonic hand is a regrasp control and is developed based on hybrid control since our system exhibits both discrete and continuous behaviors.