Abstract Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and distributed information acquisition and dissemination infrastructure. With the accelerated development of wireless Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) communications, the integrated acquiring and processing of information is becoming feasible at an increasingly large scale. Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems. It is a challenging problem due to the dynamic changes of the traffic states caused by many uncertain factors along a travelling route. In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength values of communications between pairs of vehicles. The V2VSPP was trained and evaluated by using traffic data provided by the Australian Centre for Field Robotics. It contains vehicle state information, vehicle-to-vehicle communications and road maps with high temporal resolution for large numbers of interacting vehicles over a long time period. The experimental results show that the proposed approach (V2VSPP) has the capability of providing accurate predictions of speed profiles in multi-vehicle trajectories setup.

This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

This paper presents implementation of optimal search strategy (OSS) in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration) and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.

The applications of robot are very extended and have already become classic in different branches of mass industrial production such as welding, painting by spraying, antirust protection, etc. Though the operations performed by robots in these fields are very complex, the operations of assembly are even more complex. In fact, robot assembly operations involve the process of direct solving the conflicting situations being not within the classic repetitive work. Investigations treating typical assembly duties started forty years ago (Bohman, 1994). In the meantime, it was offered a series of control mechanism of mating date. Performing assemblies depends on sensation of and appropriate reaction to the forces of contact between mating components date (Wei, 2001). It is shown that with the intelligent techniques, example components can be assembled faster, gentle and more reliably. In order to create robot behaviours that are similarly intelligent, we seek inspiration from human strategies date (Chan, 1995). The working theory is that the human accomplishes an assembly in phases, with a defined behaviour and a subgoal in each phase. The human changes behaviours according to events that occur during the assembly and the behaviour is consistent between the events. The human’s strategy is similar to a discrete event system in that the human progresses through a series of behavioural states separated by recognizable physical events. In achieving acceptably fast robot behavior with assuring contact stability, many promising intelligent-control methods have been investigated in order to learn unstructured uncertainties in robot manipulators date (Chan, 1995), (Miyazaki et al., 1993), (Brignone et al., 2001). For example, (Newman et al., 2001) work describes intelligent mechanical assembly system. First phase for assembly is blind search. In this phase multiple parameters are assigned to rotational search attractor. If sensors register force values higher then thresholds, new parameters are assigned. Intelligent layer is represented on 22-dimensional space of trajectories, and based on blind search parameters (correct and incorrect) neural network is made. Correct assembly path is chosen by using form of Genetic algorithm search, so the new vectors are evolved from most successful “parents”. Using this process, the robot was allowed to generate and test its own program modifications. The primary source of difficulty in automated assembly is the uncertainty in the relative position of the parts being assembled (Vaaler, 1991). The crucial thing in robot assembly is how to enable a robot to accomplish a task successfully in spite of the inevitable uncertainties