Giorgio Guglieri

Path Planning Strategies for UAVS in 3D Environments

The graph-search algorithms developed between 60s and 80s were widely used in many fields, from robotics to video games. The A* algorithm shall be mentioned between some of the most important solutions explicitly oriented to motion-robotics, improving the logic of graph search with heuristic principles inside the loop. Nevertheless, one of the most important drawbacks of the A* algorithm resides in the heading constraints connected with the grid characteristics. Different solutions were developed in the last years to cope with this problem, based on post-processing algorithms or on improvements of the graph-search algorithm itself. A very important one is Theta* that refines the graph search allowing to obtain paths with “any” heading. In the last two years, the Flight Mechanics Research Group of Politecnico di Torino studied and implemented different path planning algorithms. A Matlab based planning tool was developed, collecting four separate approaches: geometric predefined trajectories, manual waypoint definition, automatic waypoint distribution (i.e. optimizing camera payload capabilities) and a comprehensive A*-based algorithm used to generate paths, minimizing risk of collision with orographic obstacles. The tool named PCube exploits Digital Elevation Maps (DEMs) to assess the risk maps and it can be used to generate waypoint sequences for UAVs autopilots. In order to improve the A*-based algorithm, the solution is extended to tri-dimensional environments implementing a more effective graph search (based on Theta*). In this paper the application of basic Theta* to tri-dimensional path planning will be presented. Particularly, the algorithm is applied to orographic obstacles and in urban environments, to evaluate the solution for different kinds of obstacles. Finally, a comparison with the A* algorithm will be introduced as a metric of the algorithm performances.

A Minimum Risk Approach for Path Planning of UAVs

In the last years, the Flight Mechanics Research Group of Politecnico di Torino started a wide research activity, focused on exploration and implementation of path planning algorithms for commercial autopilots, typically adopted on unmanned vehicles. Different path planning approaches were implemented in a Matlab/Simulink based tool, generating waypoints sequences with four methods: geometric predefined trajectories, manual waypoints definition, automatic waypoints distribution (i.e. optimizing camera payload capabilities) and, finally, a comprehensive A*-based approach. The tool was also integrated with functions managing the maps used for planning. In this paper, two approaches to path planning in presence of orographic obstacles are detailed. The first algorithm is subdivided in three phases: the generation of a risk map associated with the ground orography, the transformation of the map in a digraph analyzed with the A* algorithm (to obtain the path with minimum risk/minimum distance) and finally the smoothing phase, to obtain a flyable waypoint sequence, realized with the Dubins curves. The structure of this method was defined and implemented, but its optimization is still in progress. The second algorithm is based on the same risk map, but optimizing polynomial curves with a genetic algorithm. This method produces a flyable waypoints sequence, minimizing a cost function reflecting path length and collision risk. An extension of this path solver, including aircraft performance constraints, was also considered. This method was tested on sample domains and its computational cost has still to be evaluated before the implementation in the tool.

Advanced Graph Search Algorithms for Path Planning of Flight Vehicles

Path planning is one of the most important tasks for mission definition and management of manned flight vehicles and it is crucial for Unmanned Aerial Vehicles (UAVs) that have autonomous flight capabilities. This task involves mission constraints, vehicle’s characteristics and mission environment that must be combined in order to comply with the mission requirements. Nevertheless, to implement an effective path planning strategy, a deep analysis of various contributing elements is needed. Mission tasks, required payload and surveillance systems drive the aircraft selection, but its characteristics strongly influence the path. As an example, quad-rotors have hovering capabilities. This feature permits to relax turning constraints on the path (which represents a crucial problem for fixed-wing vehicles). The type of mission defines the environment for planning actions, the path constraints (mountains, hills, valleys, ...) and the required optimization process. The need for off-line or real-time re-planning may also substantially revise the path planning strategy for the selected type of missions. Finally, the computational performances of the Remote Control Station (RCS), where the mission management system is generally running, can influence the algorithm selection and design, as time constraints can be a serious operational issue.

NMPC and genetic algorithm-based approach for trajectory tracking and collision avoidance of UAVs

Research on unmanned aircraft is improving constantly the autonomous flight capabilities of these vehicles in order to provide performance needed to employ them in even more complex tasks. UAV path planning PP system plans the best path to perform the mission and then it uploads this path on the flight management system FMS providing reference to the aircraft navigation. Tracking the path is the way to link kinematic references related to the desired aircraft positions with its dynamic behaviours, to generate the right command sequence. This paper presents a non-linear model predictive control NMPC system that tracks the reference path provided by PP and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves online i.e., at each sampling time a finite horizon state horizon open loop optimal control problem with a genetic algorithm. This algorithm finds the command sequence that minimises the tracking error with respect to the reference path, driving the aircraft far from sensed obstacles and towards the desired trajectory.