Using On-line Simulation in UAV Path Planning

In this thesis, we investigate the problem of Unmanned Aerial Vehicle (UAV) path planning in search or surveillance mission, when some a priori information about the targets and the environment is available. A search operation that utilizes the available a priori information about the initial location of the targets, terrain data, and information from reasonable assumptions about the targets movement can in average perform better than a uniform search that does not incorporate this information. This thesis provides a simulation-based framework to address this type of problem. Search operations are generally dynamic and should be modified during the mission due to new reports from other sources, new sensor observations, and/or changes in the environment, therefore a Symbiotic Simulation method that employs the latest data is suggested. All available information is continuously fused using Particle Filtering to yield an updated picture of the probability density of the target. This estimation is used periodically to run a set of what-if simulations to determine which UAV path is most promising. From a set of different UAV paths the one that decreases the uncertainty about the location of the target is preferable. Hence, the expectation of information entropy is used as a measure for comparing different courses of action of the UAV. The suggested framework is applied to a test case scenario involving a single UAV searching for a single target moving on a road network. The performance of the Symbiotic Simulation search method is compared with an off-line simulation and an exhaustive search method using a simulation tool developed for this purpose. The off-line simulation differs from the Symbiotic Simulation search method in that in the former case the what-if simulations are conducted before the start of the mission. In the exhaustive search method the UAV searches the entire road network. The Symbiotic Simulation shows a higher performance and detects the target in the considerably shorter time than the other two methods. Furthermore, the detection time of the Symbiotic Simulation is compared with the detection time when the UAV has the exact information about the initial location of the target, its velocity and its path. This value provides a lower bound for the optimal solution and gives another indication about the performance of the Symbiotic Simulation. This comparison also suggests that the Symbiotic Simulation in many cases achieves a “near” optimal performance.