Reactive route selection from pre-calculated trajectories – application to micro-UAV path planning

Abstract

must be robust to changes in the local mapped environment. However, for many scenarios typical of UAV operations, it is still desirable to implement waypoint-driven guidance trajectories off-line to ensure optimality of the chosen flight profile. This is particularly true of lightweight micro-UAVs where optimal path planning is essential to maximise operational effectiveness against the low range and endurance induced by weight constraints on the powerplant.Path planning for UAVs, both on-line and off-line, has been an active research area for well over the past decade or more (3)(4)(5) . Many approaches have been proposed using techniques developed from a variety of different engineering and computing disciplines, however all techniques can be classified as belonging to one of two types: either a globally optimal or locally adaptive process. Locally adaptive algorithms are very popular with the mobile robotics community forming an integral part of the wider activity of selflocalisation and mapping (SLAM) (6) . Many such techniques are based upon graph theoretic algorithms originally developed within the artificial intelligence community such as Dijkstra, A*, D* etc. (7 8) and more recent graph or search-tree construction algorithms based on random searches such as the Rapidly-exploring Random Tree (RRT) algorithm (9) . However, tree construction and search can be a computationally intensive process if the desired path trajectories are of high dimension, for example when position, rate, attitude, orientation, time and other kinematic constraints are required. The resulting computational constraints mean that finding the optimal path by such methods is due more to luck than design.Global, optimal waypoint-driven path-planning techniques are ABSTRACTOperating micro-UAVs autonomously in complex urban areas requires that the guidance algorithms on-board are robust to changes in the operating environment. Limited endurance capability demands an optimal guidance algorithm, which will change as the environment does. All optimal path-planning routines are computationally intensive, with processor load a function of the environmental complexity. This paper presents a new algorithm, the reactive route selection algorithm, for storing a bank of optimal trajectories computed off-line and blending between these optimal trajectories as the operating environment changes. An example is presented using a mixed-integer linear program to generate the optimal trajectories.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

“…For instance, our search included, but was not limited to, such terms as “unmanned aerial vehicle,” “UAV,” “drone,” “unmanned aircraft,” “unmanned aerial system,” “UAS,” “remotely piloted aircraft,” and “remotely piloted vehicle.” Note that operations planning of drones are closely related to more general topics, such as operations planning of mobile robots (including ground‐based drones) and mobile sensors as well as vehicle routing and machine scheduling, which are not part of this survey. We consider complex drone operations that usually involve several tasks. Thus, we do not include articles on obstacle‐avoiding path planning from a given starting point to a given end point or on planning trajectories of drones flying in formation . We refer the interested reader to the work of Goerzen et al , Khaksar et al and Yang et al on path planning problems. We survey articles that formulate an optimization problem, describing objective functions, decision variables, and the set of feasible solutions.…”

DIMPL: a distributed in-memory drone flight path builder system

“…Landmark based visual navigation for use in cases of GPS failure has also been investigated [6], along with using network connections to control fleet of drones [46]. Reactive route selection from a bank of optimal trajectories based on changes in operating environments have been proposed [25], and an A* algorithm to plan shortest flight paths for drones adaptive to different flight conditions [37].…”

A CFD-based procedure for airspace integration of small unmanned aircraft within congested areas

“…We can see that the symmetrical nature of this simplified urban environment generates symmetrical flow profiles with clearly defined corridors of low TKE that are considered navigable when utilising a 2D A* algorithm or some other optimal guidance scheme. [28][29][30] This is not representative of most cities where buildings can: be of unequal height; differ in their geometry and be distributed in a non-uniform manner. Ideally, when considering integration of micro-UAVs within an urban airspace, an analysis of the turbulence generated should be carried out for each inhabitable region of the city that cannot be generalised and is unique to that city alone.…”