We present a parallel formulation for enumerative search in high dimensional spaces and apply it to planning paths for a 6-dof manipulator robot. Participating processors perform local A* search towards the goal configuration. To exploit all the processors at their maximum capacity at all times, a dynamic load-balancing scheme matches idle and busy processors for load transfer. For comparison purposes, we have also implemented an existing parallel static load-balancing formulation based on regular domain decomposition. Both methods achieved almost linear speed-up in our experiments. The two methods follow different search strategies in parallel and the implementation of the existing method (with tuned space decomposition) was more time efficient on average. However, the planning time of that method is highly dependent on the distribution of the search space among the processors and its tuned decomposition varies for different obstacle placements. Empirical selection of the space decomposition parameters for the existing method does not guarantee minimal planning time in all environments and leads to slower planning than our dynamic load-balancing method in some cases. The performance of the developed dynamic method is independent of the obstacle placements and the method can achieve consistent speed-up in all environments.

One of the problems in the field of mobile robotics is the estimation of the robot position in an environment. This paper proposes a model for estimating a confidence interval of the robot position in order to compare it with the estimation made by a dead-reckoning system. Both estimations are fused using heuristic rules. The positioning model is very valuable in estimating the current robot position with or without knowledge about the previous positions. Furthermore, it is possible to define the degree of knowledge of the robot previous position, making it possible to adapt the estimation by varying this knowledge degree. This model is based on a one-pass neural network which adapts itself in real time and learns about the relationship between the measurements from sensors and the robot position.

Human can handle a deformable object and damp its vibration with recognized skill. However, for an industrial robot, handling a deformable object with acute vibration is often a difficult task. This paper addresses the problem of active damping skill for handling deformable linear objects (DLOs) by using a strategy inspired from human manipulation skills. The strategy is illustrated by several rules, which are explained by a fuzzy and a P controller. A proportional-integral-derivative (PID) controller is also employed to explain the rules as a comparison. The interpretations from controllers are translated into high level commands in a robotic language V+. A standard industrial robot with a force/torque sensor mounted on the wrist was employed to demonstrate the skill. Experimental results showed the fuzzy based damping skill is quite effective and stable even without any previous acknowledge of the deformable linear objects.

This paper compares Neurolinear feature extraction technique to two other feature extraction techniques. The author has developed all three methods recently, but in this paper, Neurolinear method has been improved even further. The study is performed in respect to performance, accuracy and suitability for visualisation. Experimental study is performed on a variety of datasets from numerous domains: Chemistry, biology, finance etc. In total, 19 real and two artificial datasets were used. Comparison is performed in terms of computational efficiency and accuracy. Results show that Neurolinear method achieves best accuracy while Best directions method is not so accurate but often much faster.

Despite major advances in Autonomous Underwater Vehicle design, the manually operated Underwater Vehicle (ROV) is still very much the industry workhorse. Current technologies are being used to reduce the stress of direct task operations by providing autonomy and to improve efficiency. This paper presents a design of a control module subsystem for a VE tele-operated ROV system. It discusses the design and implementation of the control module. Using modelling, simulation and experiments, the vehicle model and its parameters have been identified. These are used in the analysis and design of closed loop stabilising controllers for station keeping. As the vehicle has fewer actuators than possible degrees of freedom, it is necessary to limit the controllable degrees of freedom. These variables are eventually selected based on the inherent vehicle dynamics. Using the Lyapunov direct method, appropriate stabilising controllers have been designed. The station-keeping mode controller has PID structure and its gain values are designed using a non-linear optimising approach. Simulation and swimming pool tests for the heave and yaw directions have shown that the control module is able to provide reasonable depth and heading station keeping.

We have been developing a network distributed Human-Assistance Robotic System in order to improve care cost and the QoL (Quality of Life) of the elderly people in the population-aging society. We developed multi-functional robotic system and implemented several CORBA application servers to provide some basic services to aid the aged or disabled. A novel method of localization of mobile robot with a camera and RFID (Radio Frequency Identification) technology is proposed as it is inexpensive, flexible and easy to use in practical environment. A video/audio conference system is also developed to improve the interaction among the users, switch robot manipulating privilege with the help of a centralized user management server, and enable a web-user to get a better understanding of what is going on in the local environment. Considering multi-type user of the developed, we have implemented multiple HRIs (Human Robot Interfaces) that enable different user to control robot systems easily.

Several indexes for evaluation of nonlinear effects during the motion of rigid multi-body systems are proposed in this paper. All of them are based on the kinetic energy and arise from an analysis of equations of motion expressed in terms of inertial quasi-velocities (IQV). Such equations are first-order and have a diagonal mass matrix of the system. The IQV depend on configuration of the system and take into consideration also its geometrical and inertial parameters. The indexes enable to detect deformation of joint velocity as a consequence of manipulator motion, the kinetic energy resulting from dynamical couplings and the energy transferred by each link individually. Because calculation of these indexes allow to determine influence of couplings on behavior of the manipulator then they can be used in the design phase for analysis and reduction of nonlinear effects in any motion. Simulation and experimental results demonstrate that the proposed indexes are significant for evaluation of nonlinear effects and kinetic energy reduction during the manipulator motion.

In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.

A kinetic energy approach to PD control in joint space of a manipulator in terms of the eigenfactor quasi-coordinate velocity (EQV) vector is considered in this paper. The modified PD controller which contains quantities resulting from decomposition of a manipulator mass matrix is proposed. The obtained equations of motion are based on the eigenvalues and eigenvectors of the mass matrix (Junkins, J. L. and Schaub, H. [7]. It is shown that utilizing the EQV vector one can determine directly the kinetic energy of the manipulator and at the same time realize PD control in its joint space. This energy-based strategy gives an interesting insight into position control. The controller presented here was tested in simulation on a 3 d.o.f. direct drive arm manipulator and via experiment on a 2 d.o.f. manipulator. The results confirmed that one can directly determine the kinetic energy for the total manipulator as well for its each joint. Additionally, time response of the system under EQV controller is faster than for the classical controller if mechanical coupling are strong.