ADVANCES IN BlOROBOTIC VlSlON

The Australian National University Biorobotic Vision Group has described its innovative researches and developments in the field of robotic vision. The group has been researching the principles by which insects see, control flights and navigate, in order to transfer their findings to their work in robotic vision. They believe this work will help them in their aim to devise algorithms for machine vision and autonomous, visually guided robots. In particular, this work is in reference to using optic flow to measure image motion.

The researchers have already successfully developed a hover controller that can hover over an arbitary set of landmarks with no manual intervention.They have also designed an autonomous helicopter that will hover and fly without using GPS.

The purpose of the present study is to assess the effect of an auxiliary lateral image and display devices on manipulation performance in a virtual reality-based hand rehabilitation system. The system consists of a personal computer, a tracker, a data glove, and a display device. For this study, a projector, a monitor, and a head-mounted display were respectively used as the display devices to present three-dimensional virtual environments. Twelve volunteers were recruited to take a pick-and-place procedure at different levels of difficulty. Task time and collision frequency were the parameters used to evaluate the manipulation performance. It was found that the presence of an auxiliary lateral image was a significant factor only for the performance of the projector group and the monitor group. In addition, no statistically significant difference was found in the comparison between the projector group and the monitor group.

This paper presents a control law for the tracking of a cyclic reference path by an under-actuated biped robot. The robot studied is a five-link planar biped. The degree of under-actuation is one during the single support phase. The control law is defined in such a way that only the geometric evolution of the biped configuration is controlled, but not the temporal evolution. To achieve this objective, we consider a parametrized control. When a joint path is given, a five degree of freedom biped in single support becomes similar to a one degree of freedom inverted pendulum. The temporal evolution during the geometric tracking is completely defined and can be analyzed through the study of a model with one degree of freedom. Simple analytical conditions, which guarantee the existence of a cyclic motion and the convergence towards this motion, are deduced. These conditions are defined on the reference trajectory path. The analytical considerations are illustrated with some simulation results.

This paper presents two frontal plane algorithms for 3D dynamic bipedal walking. One of which is based on the notion of symmetry and the other uses reinforcement learning algorithm to learn the lateral foot placement. The algorithms are combined with a sagittal plane algorithm and successfully applied to a simulated 3D bipedal robot to achieve level ground walking. The simulation results showed that the choice of the local control law for the stance-ankle roll joint could significantly affect the performance of the frontal plane algorithms.

In this paper the mechanical design for a new 7-dofs leg is investigated as a walking module for a humanoid robot. In particular, a dynamic simulation is deduced by means of a Newton-Euler formulation and implemented numerically in order to compute the needed input actuator torques. A simulation is carried out for a similar built leg prototype that is used as a walking module for WABIAN-RIV (WAseda BIped humANoid robot-Revised IV). Experimental tests are carried out on this existing leg in order to validate the proposed formulation for a similar application. Thus, the validated formulation has been used in order to design the actuators for a new leg prototype.

To fully utilize the information from the sensors, this paper proposes a new sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable an accurate measurement. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar-sensing system or the visual-sensing system. Note that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the accurate measurement. The theoretical basis is illustrated by examples and the effectiveness is proved through the simulations and experiments. The newly proposed, STSF (Space and Time Sensor Fusion) scheme is applied to the navigation of a mobile robot in an unstructured environment, as well as in structured environment, and the experimental results show the performance of the system.

In this paper, we demonstrate a multi-phase genetic programming (MPGP) approach to an autonomous robot learning task, where a sumo wrestling robot is required to execute specialized pushing maneuvers in response to different opponents' postures. The sumo robot used has a very simple, minimalist hardware configuration. This example differs from the earlier studies in evolutionary robotics in that the former is carried out on-line during the performance of a robot, whereas the latter is concerned with the evolution of a controller in a simulated environment based on extended genetic algorithms. As illustrated in several sumo maneuver learning experiments, strategic maneuvers with respect to some possible changes in the shape and size of an opponent can readily emerge from the on-line MPGP learning sessions.

In this paper, the optimum design of parallel kinematic toolheads is implemented using genetic algorithms with the consideration of the global stiffness and workspace volume of the toolheads. First, a complete kinetostatic model is developed which includes three types of compliance, namely, actuator compliance, leg bending compliance and leg axial compliance. Second, based on this model, two kinetostatic performance indices are introduced to provide a new means of measuring compliance over the workspace. These two kinetostatic performance indices are the mean value and the standard deviation of the trace of the generalized compliance matrix. The mean value represents the average compliance of the Parallel Kinematic Machines over the workspace, while the standard deviation indicates the compliance fluctuation relative to the mean value. Third, design optimization is implemented for global stiffness and working volume based on kinetostatic performance indices. Additionally, some compliance comparisons between Tripod toolhead and other two principal Tripod-based Parallel Kinematic Machines are conducted.

A novel affect-sensitive human-robot cooperative framework is presented in this paper. Peripheral physiological indices are measured through wearable biofeedback sensors to detect the affective state of the human. Affect recognition is performed through both quantitative and qualitative analyses. A subsumption control architecture sensitive to the affective state of the human is proposed for a mobile robot. Human-robot cooperation experiments are performed where the robot senses the affective state of the human and responds appropriately. The results presented here validate the proposed framework and demonstrate a new way of achieving implicit communication between a human and a robot.

This paper deals with an optimum synthesis of planar parallel manipulators using two constrained optimisation procedures based on the minimization of: (i) the overall deviation of the condition number of manipulator Jacobian matrix from the ideal/isotropic condition number, and (ii) bearing forces throughout the manipulator workspace for force balancing. A revolute jointed planar parallel manipulator is used as an example to demonstrate the methodology. The parameters describing the manipulator geometry are obtained from the first optimisation procedure, and subsequently, the mass distribution parameters of the manipulator are determined from the second optimisation procedure based on force balancing. Optimisation results indicate that the proposed optimisation approach is systematic, versatile and easy to implement for the optimum synthesis of the parallel manipulator and other kinematic chains. This work contributes to previously published work from the point of view of being a systematic approach to the optimum synthesis of parallel manipulators, which is currently lacking in the literature.

This paper describes current progress in a project to develop robotic systems for locating underground chemical sources. There are a number of economic and humanitarian applications for this technology. Finding unexploded ordinance, land mines, and sources of leaks from pipes and tanks are some examples. Initial experiments were conducted using an ethanol chemical source buried in coarse sand. To gain an understanding of the sensory environment that would be experienced by a robot burrowing through the ground, the factors affecting transport of chemical vapour through soil were investigated. A robot search algorithrn was then developed for gathering chemical gradient inforrnation and using this to guide a robot towards the source. Experiments were performed using a chemical sensing probe positioned by a UMI RTX robot manipulator arm. The resulting system was successful in locating a source of ethanol vapour buried in sand. This paper includes details of experiments to characterise the sand used in this project, the robot search algorithm, sensor probe and results of source location trials.

A new unified motion planning algorithm for autonomous Underwater Vehicle-Manipulator Systems (UVMS) has been presented in this paper. Commonly, a UVMS consists of two sub-systems, a vehicle and a manipulator, having vastly different dynamic responses. The proposed algorithm considers the variability in dynamic bandwidth of the complex UVMS system and generates not only kinematically admissible but also dynamically feasible reference trajectories. Additionally, this motion planning algorithm exploits the inherent kinematic redundancy of the whole system and provides reference trajectories that accommodates other important criteria such as thruster/actuator faults and saturations, and also minimizes hydrodynamic drag. Effectiveness of the proposed unified motion planning algorithm has been verified by extensive computer simulation. The results are quite promising.