This paper presents the parallelization of grid-based recursive Bayesian estimation (RBE) using a graphics processing unit (GPU) for real-time control of autonomous vehicles. Although the grid-based method has been effectively used for autonomous search due to its ability to represent search space explicitly, heavy computational load has been a bottleneck for real-time application similarly to other non-Gaussian RBE techniques. The proposed RBE, which parallelizes grid-wise computations using GPU upon the analysis of mathematical operations, removes sequential processes and accelerates RBE significantly. Numerical examples have first demonstrated the validation of the proposed RBE and investigated its performance through parametric studies. The proposed RBE was then applied to the cooperative search by autonomous unmanned ground vehicles (UGVs), and its real-time capability has been demonstrated.

This paper presents the updateable probabilistic evacuation modeling (UPEM) technique, which allows sensor observation data to be included in the problem of estimating the state of an evacuating crowd, as the data are obtained. Each individual is modeled as a Newtonian particle which interacts with obstacles, such as walls and other individuals. The UPEM technique estimates not only the general trend of the crowd as a whole, but also the specific states of each of the evacuees in the crowd. Furthermore, an approach to cooperative autonomous searching in crowded urban emergencies is developed using UPEM. A number of simulated searches in emergency evacuations highlight the efficacy of the technique in reducing the time required to detect targets and in increasing the level of safety for human evacuees.

This paper presents the element-based method, which can be used for recursive Bayesian estimation (RBE) in robotic operations such as search and tracking involving moving targets. The use of shape functions to define a set of irregularly shaped elements allows the target PDF to be continuously, and thus accurately, represented over the target space. A comparison with the grid-based method first shows that the element-based method requires less than 10% of the number of nodes to achieve the same accuracy. The application of the element-based method to marine search-and-rescue (SAR) scenarios then demonstrates its ability for effective SAR whilst maintaining collected information.

This paper presents a coordinated control technique that allows heterogeneous vehicles to autonomously search for and track multiple targets using recursive Bayesian filtering. A unified sensor model and a unified objective function are proposed to enable search-and-tracking (SAT) within the recursive Bayesian filter framework. The strength of the proposed technique is that a vehicle can switch its task mode between search and tracking while maintaining and using information collected during the operation. Numerical results first show the effectiveness of the proposed technique when a found target becomes lost and must be searched for again. The proposed technique was then applied to a practical marine search-and-rescue (SAR) scenario where heterogeneous vehicles coordinated to search for and track multiple targets. The result demonstrates the applicability of the technique to real search world scenarios