Abstract: This paper presents a robotics vision-based heuristic reasoning system for underwater target tracking and navigation. This system is introduced to improve the level of automation of underwater Remote Operated Vehicles (ROVs) operations. A prototype which combines computer vision with an underwater robo tics system is successfully designed and developed to perform target tracking and intelligent navigation. Th is study focuses on developing image processing algorithms and fuzzy inference system for the analys is of the terrain. The visi on system developed is capable of interpreting underwater scene by extracting subjective uncertainties of the object of interest.

The problem of designing optimal trajectory for redundant manipulators has attracted many researchers for the last three decades. One of the main reasons is the use of kinematically redundant robots is expected to increase in the future due to their increased flexibility. Some of the extra capabilities include the ability to avoid internal singularities or exte rnal obstacles over their entire workspace (Parket et al.,1989). Also, the inverse kinematics problem is underdetermined and admits an infinite number of distinct feasible solutions, meaning that a given end-effector pos es can be realized by an infinite number of distinct manipulator configurations (McAvoy, et al, 2000). In order to overcome the shortcomings inherent in non-redundant robots, redundant robots have been utilized in industrial applications to increase fl exibility and dexterity around a restricted task space in pres ence of obstacle.

A good organization model of multiple mobile robot should be able to improve the efficiency of the system, reduce the complication of robot interactions, and detract the difficulty of computation. From the sociology aspect of topology, structure and organization, this paper studies the multiple mobile robot organization formation and running mechanism in the dynamic, complicated and unknown environment. It presents and describes in detail a Hierarchical- Web Recursive Organization Model (HWROM) and forming algorithm. It defines the robot society leader; robotic team leader and individual robot as the same structure by the united framework and describes the organization model by the recursive structure. The model uses task-oriented and top-down method to dynamically build and maintain structures and organization. It uses market-based techniques to assign task, form teams and allocate resources in dynamic environment. The model holds several characteristics of self-organization, dynamic, conciseness, commonness and robustness.

At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative mechanism, and discuss how to optimize relative action sets during the evolution. The dynamic evolution model of robot team and cooperative relationships between robot teams proposed and described in this paper can not only generalize the robot team as a whole, but also depict the dynamic evolving process quantitatively. Users can also make the prediction of the cooperative relationship and the action of the robot team encountering new demands based on this model. Journal web page & a lot of robotic related papers www.ars-journal.com

A resolved acceleration control (RAC) and proportional-integral active force control (PIAFC) is proposed as an approach for the robust motion control of a mobile manipulator (MM) comprising a differentially driven wheeled mobile platform with a two-link planar arm mounted on top of the platform. The study emphasizes on the integrated kinematic and dynamic control strategy in which the RAC is used to manipulate the kinematic component while the PIAFC is implemented to compensate the dynamic effects including the bounded known/unknown disturbances and uncertainties. The effectivenss and robustness of the proposed scheme are investigated through a rigorous simulation study and later complemented with experimental results obtained through a number of experiments performed on a fully developed working prototype in a laboratory environment. A number of disturbances in the form of vibratory and impact forces are deliberately introduced into the system to evaluate the system performances. The investigation clearly demonstrates the extreme robustness feature of the proposed control scheme compared to other systems considered in the study.

This paper investigates the problem of the dynamic balance control of multi-arm free-floating space robot during capturing an active object in close proximity. The position and orientation of space base will be affected during the operation of space manipulator because of the dynamics coupling between the manipulator and space base. This dynamics coupling is unique characteristics of space robot system. Such a disturbance will produce a serious impact between the manipulator hand and the object. To ensure reliable and precise operation, we propose to develop a space robot system consisting of two arms, with one arm (mission arm) for accomplishing the capture mission, and the other one (balance arm) compensating for the disturbance of the base. We present the coordinated control concept for balance of the attitude of the base using the balance arm. The mission arm can move along the given trajectory to approach and capture the target with no considering the disturbance from the coupling of the base. We establish a relationship between the motion of two arm that can realize the zeros reaction to the base. The simulation studies verified the validity and efficiency of the proposed control method.

Its capabilities cover a wide range of possible styles of algorithms. It is al so a standard environment for testing different techniques of making standard software agents with distributed architecture[10]. Rescue Simulation System also prov ides a standard framework for testing proposed algorithms and mathematical models of disaster events[8]. Designing an autonomous agent set like the one that is required for RoboCup Rescue Simulation is a little bit more of a challenge. Planning effective collaboration for a Multi-Agent team in disastrous environments still remains a challenging area in AI. Efforts of Multi-Agent researchers have provided somewhat of a standard in modeling and designing software. A lot of effort has gone into reaching coordination between different agents and making autonomous decisions that work toward the team goal[9]. But practical results in complicated domains such as RoboCup Rescue Simulation indicate that heuristic criteria still remain as a major part of a successful system[11]. This may signal lack of satisfactory models for these complicated situations.

It is very important for quadruped walking machine to keep its stability in high speed walking. It has been indicated that moment around the supporting diagonal line of quadruped in trotting gait largely influences walking stability. In this paper, moment around the supporting diagonal line of quadruped in trotting gait is modeled and its effects on body attitude are analyzed. The degree of influence varies with different initial stances of quadruped and we get the optimal initial stance of quadruped in trotting gait with maximal walking stability. Simulation results are presented. Keywords: quadruped, trotting, attitude, walking stability.

Research on integrated neural-symbolic systems has made si gnificant progress in the recent past. In particular the understanding of ways t o deal with symbolic knowledge within connectionist systems (also cal led artificial neural networks) has reached a critical mass which enables the c ommunity to strive for applicable implementations and use cases. Recen t work has covered a great variety of logics used in artificial intelligenc e and provides a multitude of techniques for dealing with them within the con text of artificial neural networks. Already in the pioneering days of computational models of ne ural cognition, the question was raised how symbolic knowledge can be r epresented and dealt with within neural networks. The landmark paper [M cCulloch and Pitts, 1943] provides fundamental insights how propositional logic can be processed using simple artificial neural networks. Within the following decades, however, the topic did not receive much attention as research in artifi cial intelligence initially focused on purely symbolic approaches. The power of machine learning using artificial neural networking was not recogni zed until the 80s, when in particular the backpropagation algorithm [Rumelha rt et al., 1986] made connectionist learning feasible and applicable in pra ctice. These advances indicated a breakthrough in machine learnin g which quickly led to industrial-strength applications in areas s uch as image analysis, speech and pattern recognition, investment analysis, engine monitoring, fault diagnosis, etc. During a training process from raw dat a, artificial neural networks acquire expert knowledge about the problem dom ain, and the ability to generalize this knowledge to similar but previou sly unencountered situations in a way which often surpasses the abilities of hu man experts.

The synchronization of two stochastically coupled one-dim ensional cellular automata (CA) is analyzed. It is shown that the transition to synchronizatio n is characterized by a dramatic increase of the statistical complexity of the patterns generated by t he difference automaton. This singular behavior is verified to be present in several CA rules display ing complex behavior. Despite all the efforts devoted to understand the meaning of complexity, we still do not have an instrument in the laboratories specially designed for quantifying this property. M aybe this is not the final objective of all those theoretical attempts carried out in the most diverse fields of know ledge in the last years [1, 2, 3, 4, 5, 6, 7, 8], but, for a moment, let us think in that possibility.