Skip to main content
Article thumbnail
Location of Repository

Unmanned aerial vehicle route planning on a dynamically changing waypoint based map for exploration purposes

By G. P. Kladis, J. T. Economou, Antonios Tsourdos, B. A. White and K. Knowles


In the included work the Unmanned Aerial Vehicle (UAV) mission is represented by energy graphs motivated by the analysis in [1]. The problem of the shortest path routing is revisited when a dynamically changing environment is considered. It is assumed that information about the map is received while on flight due to events. In addition, UAVs are required, while on mission, to "scout" areas of interest which involves extracting as much intelligence as possible and traversing it in the most safe flyable means. Hence, the UAV should be capable of integrating knowledge from a variety of sources and re-plan its mission accordingly in order to fulfil objectives. Motivated by the previous, depending on the decision making process, the notion of a "temporary" optimum path can be of physical and functional sense. The problem is modeled as a multistage decision making process, where each stage is triggered by an event and is characterized by a current starting point, an area for reconnaissance purposes and a final destination. Hence, given the current availability between paths, the objective is to devise a policy that leads from an origin or current known location to a destination node while traversing the unknown region of interest with the minimal energy demand

Year: 2009
OAI identifier:
Provided by: Cranfield CERES

Suggested articles


  1. (2007). A node-to-node composite graph and pseudo-boolean modeling: A uav energy application.” doi
  2. (1959). A note on two problems in connexion with graphs.” doi
  3. (2008). Aerospace energy conservation utilizing optimum methods,” doi
  4. (1962). Algorithm 97: Shortest path algorithm.” doi
  5. (1968). An appraisal of some shortest-path algorithms,” doi
  6. (2008). An emergency refueling problem over a dynamically changing environment in the context of unmanned aerial vehicles.” doi
  7. (1992). Analysis of shortest-path routing algorithms in a dynamic network environment,” doi
  8. (1972). Determination of shortest path in a network with time-dependent edge-lengths.” doi
  9. (1989). Genetic Algorithms in Search Optimization and Machine Learning. doi
  10. (1991). Handbook of Genetic Algorithms. doi
  11. (1957). On curves of minimal length with a constraint on average curvature and with perscribed initial and terminal positions and tangent.” doi
  12. (2007). Optimality and reachability - pseudo boolean power flows for a milti-sourced vehicle topologies,” doi
  13. (1972). Reducibility among combinatorial problems,” doi
  14. (1980). Reoptimization procedures in shortest path problem,” doi
  15. (1991). Shortest paths without a map,” doi
  16. (1990). Shortest-path and minimum-delay algorithms in networks with time-dependent edge length,” doi
  17. (1966). The shortest route through a netwozk with time-dependent inter- nodal transit times,” doi
  18. (1995). Time depending shortest-path problems with applications to railway networks.” doi
  19. (2008). Uav mission selection for optimum routing process,”
  20. (1985). Updating distances in dynamic graphs,”

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.