Supporting drivable region detection by minimising salient pixels generated through robot sensors

The role of robots, automatically guided machines able to perform tasks on their own cannot be over emphasized. In particular, if robotic vehicles are to work effectively, the way they are required to perform their jobs and their ability to reach the desired destination where the job is to be performed are of utmost importance. This necessitates the need to facilitate proper navigational aid for robotic vehicles. Various navigational approaches have been proposed in robotics literature, but this work serves to provide an assistive pre-processing strategy for the detection of drivable region through minimisation of salient pixels in a colour feature extraction. Salient pixels are pixels occupying the non-drivable region particularly those having same grayscale value as road images. Salient pixels provide difficulties during colour feature extraction on road images captured by a robot’s camera (sensor). In our method, a stream of road images is captured, pixels are extracted based on a RGB (red, green, blue) colour space, edges of objects are detected using Sobel operator. Salient pixels are minimised using some heuristic which is based on a threshold parameter. In a series of experiments using our method, a stream of real life road images is obtained and results show that good drivable regions, which facilitate proper robotic navigation, can be detected

Emergent Future Situation Awareness: A Temporal Probabilistic Reasoning in the Absence of Domain Experts

Dynamic Bayesian networks (DBNs) are temporal probabilistic models for reasoning over time which are rapidly gaining popularity in modern Artificial Intelligence (AI) for planning. A number of Hidden Markov Model (HMM) representations of dynamic Bayesian networks with different characteristics have been developed. However, the varieties of DBNs have obviously opened up challenging problems of how to choose the most suitable model for specific real life applications especially by non-expert practitioners. Problem of convergence over wider time steps is also challenging. Finding solutions to these challenges is difficult. In this paper, we propose a new probabilistic modeling called Emergent Future Situation Awareness (EFSA) which predicts trends over future time steps to mitigate the worries of choosing a DBN model type and avoid convergence problems when predicting over wider time steps. Its prediction strategy is based on the automatic emergence of temporal models over two dimensional (2D) time steps from historical Multivariate Time Series (MTS). Using real life publicly available MTS data on a number of comparative evaluations, our experimental results show that EFSA outperforms popular HMM and logistic regression models. This excellent performance suggests its wider application in research and industries

Supporting Scalable Bayesian Networks Using Configurable Discretizer Actuators

We propose a generalized model with configurable discretizer actuators as a solution to the problem of the discretization of massive numerical datasets. Our solution is based on a concurrent distribution of the actuators and uses dynamic memory management schemes to provide a complete scalable basis for the optimization strategy. This prevents the limited memory from halting while minimizing the discretization time and adapting new observations without re-scanning the entire old data. Using different discretization algorithms on publicly available massive datasets, we conducted a number of experiments which showed that using our discretizer actuators with the Hellinger’s algorithm results in better performance compared to using conventional discretization algorithms implemented in the Hugin and Weka in terms of memory and computational resources. By showing that massive numerical datasets can be discretized within limited memory and time, these results suggest the integration of our configurable actuators into the learning process to reduce the computational complexity of modeling Bayesian networks to a minimum acceptable level