The path planning process for an unmanned aerial vehicle is a complex process due to different operating environment conditions and aircraft constraints. The particle swarm optimization algorithm is one of several heuristic techniques used to solve the path planning problem. However, it has been observed that this algorithm tends to converge prematurely to a local optimum, resulting in poor quality paths. This paper proposes an adaptive strategy for calculating the inertia weight (KPSO) of the particles in the PSO algorithm for UAV path planning in a real-world environment. The strategy was applied to a reconnaissance mission during an eruptive event based on the Popocatepetl volcano area in Mexico and was found to outperform other adaptive strategies from the literature in a specific scenario.