Waypoint Planning Algorithm Using Cost Functions for Surveillance

Abstract

This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters.

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

“…Degradation may generate communication failures between the vehicles, while the excess of pheromones may block certain locations of the area for the vehicles. Some approaches consider the local uncertainty during the coverage missions [81], while other ones explore distributed information merging, where the UAVs directly exchange information to speed up the search and enhance the performance [80]. Path replanning in a continuous UAV trajectory is also explored, where the authors fuse visual information received into a single probabilistic map [82,83].…”

“…Considering the CPP problem with aerial vehicles, several authors have explored different approaches in the literature, including real-time search methods [36], random walk [71], cellular systems [72][73][74], evolutionary computation [75,76], and swarm intelligence [77][78][79]. Coverage with uncertainty considering information points is also addressed [80][81][82][83][84][85]. Most of the approaches are pheromone-based and explore the natural behavior of ants to guide the vehicles through a grid-discretized scenario.…”

“…A waypoint planning algorithm for surveillance is presented by Lim and Bang [81]. The area of interest has the form of a hexagonal grid and contains Information Points (IP), as shown in Figure 26a.…”

An Automated Emergency Landing System for Fixed‐Wing Aircraft: Planning and Control

“…Aircraft guidance consists of two essential parts: 1) a path planning method that generates a feasible trajectory to an endpoint and 2) a path following method that leverages navigation data and target information to direct flight control. Path planning for UAVs is a widely researched topic with a variety of proposed methods, such as waypoint‐based planning (Lim & Bang, ), a receding horizon approach (Tisdale, Kim, & Hedrick, ), or simple A* algorithms (Filippis, Guglieri, & Quagliotti, ). A more exhaustive list of path planning methods can be found in the surveys by Nygards and Forskningsinstitut (Nygå rds, Skoglar, Karlholm, Björström, & Ulvklo, ) and Goerzen et al .…”

Vector field guidance for path following and arrival angle control

“…Lim and Bang suggested strategies to establish waypoints for a surveillance mission using multiple UA [3].…”