an innovative algorithm to estimate risk optimum path for unmanned aerial vehicles in urban environments

Abstract The diffusion of the Unmanned Aerial Vehicles (UAVs) requires a suitable approach to define safe flight operations. In this paper, an innovative algorithm able to quantify the risk to the population and to search for the minimum risk path is proposed. The method has two main phases: in the former, a risk-map is generated quantifying the risk of a specific area, in the latter, a path planning algorithm seeks for the optimal path minimizing the risk. The risk-map is generated with a risk assessment method combining layers related to the population density, the sheltering factor, no-fly zones and obstacles. The risk-aware path planning is based on the well-known Optimal Rapidly-exploring Random Tree, with the minimization of the risk cost with respect to the flight time. Simulation results corroborate the validity of the approach

Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV

We present an efficient path planning algorithm for an Unmanned Aerial Vehicle surveying a cluttered urban landscape. A special emphasis is on maximizing area surveyed while adhering to constraints of the UAV and partially known and updating environment. A Voronoi bias is introduced in the probabilistic roadmap building phase to identify certain critical milestones for maximal surveillance of the search space. A kinematically feasible but coarse tour connecting these milestones is generated by the global path planner. A local path planner then generates smooth motion primitives between consecutive nodes of the global path based on UAV as a Dubins vehicle and taking into account any impending obstacles. A Markov Decision Process (MDP) models the control policy for the UAV and determines the optimal action to be undertaken for evading the obstacles in the vicinity with minimal deviation from current path. The efficacy of the proposed algorithm is evaluated in an updating simulation environment with dynamic and static obstacles.Comment: Accepted at International Conference on Unmanned Aircraft Systems 201