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

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 (3-5). 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 self-localisation 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. 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 ABSTRACT Operating 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 computa-tionally 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. Arguably one of the most mission-critical components in the guidance system of airborne autonomous vehicles is the path-planning algorithm. The operational viability of the platform is crucially interwoven with the efficacy of the chosen path planning algorithm to provide timely, reactive and robust guidance commands, especially in …