Dynamic motion planning for aerial surveillance on a fixed-wing UAV

Abstract

Abstract-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.

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

“…For instance, Albert et al use a graph‐theoretical approach to assign objects (icebergs) to drones and to determine tours of the drones over these objects, so that the objects with the highest observational uncertainty are visited first. In Darbari et al , the drone must discover obstacles and predict their motion in order to cover a maximum area within the limited flight time while avoiding collisions. Several articles investigate the cyclic‐routing drone problem, in which drones fly back and forth to update information on the objects’ positions .…”

A two-stage route optimization algorithm for light aircraft transport systems

“…In the "rapidly-expanding random tree", the search algorithm generates nodes in the free space and as long as they do not fall in (or on) an obstacle, it connects them to one another (LaValle, 1998;LaValle and Kuffner, 2001). For each node, the algorithm checks the closest neighboring ones to link them if the connection is obstacle-free and feasible in a kinodynamic manner (see Frazzoli et al, 2000Frazzoli et al, , 2002Adiyatov and Varol, 2013;Gammell et al, 2014;Redding et al, 2007;Darbari et al, 2017;Dever et al, 2004).…”

Hybrid A* path search with resource constraints and dynamic obstacles

“…Path planning with dynamic obstacles allows autonomous agents to model their complex environment with a higher degree of fidelity. Incorporating dynamic obstacles within various trajectory planners continues to be a challenge due to the complexity related to frequent recomputation of the path when the environment changes (Darbari et al, 2017). This is computationally expensive and not feasible for online path planning when the agent will need to calculate and perform the next action in time to avoid colliding with a moving obstacle.…”