UAV path planning optimization based on GNSS quality and mission requirements

One of the most crucial factors for the overall success of an Unmanned Aerial Vehicle (UAV) mission is navigation performance, which is severely affected in Global Navigation Satellite Systems (GNSS) challenging environments. A solution to this problem could come through path planning optimization. This paper investigates the impact that GNSS quality information included in the UAV path planning process would have on the overall UAV mission success rate (MSR) when flying through an urban canyon. Number of visible satellites and Horizontal Dilution of Precision (HDOP) in addition to mission-specific requirements are given as input to the Particle Swarm Optimization (PSO) algorithm to calculate the optimal path for two cases. One includes the GNSS observables, and the other does not. Optimal paths for three different altitudes are obtained. All paths are simulated by a GNSS signal simulator, including a comprehensive multipath model. GNSS data are collected by a hardware receiver for analysis of the UAV positioning error and GNSS availability. Mission failures cases are defined accordingly, and the overall mission success rate (MSR) of each scenario is assessed. By analyzing the findings, it is concluded that in 83% of cases, the path planning process that included GNSS information was able to increase the MSR. Also, the increase in MSR was bigger when flying at low altitude.