Unmanned aerial vehicles (UAVs) present in a wide range of scales, airframe types and possible systems configurations. Assessing how these different systems perform, therefore, should be an essential part of their design. This task, however, is particularly difficult due to the complex, dynamic interactions these vehicles are capable of, and the often complex operational conditions. In this paper we describe and apply an evaluation framework based on spatial cost-to-go (SCTG) maps. These maps describe the spatial distributions of optimal state and cost-to-go for a given geographical environment, and are computed using a finite-state approximation of the vehicle dynamics. The SCTG maps embed the interaction effects between vehicle dynamics and environment, and thus provide a rigorous basis for the evaluation of the airframe, various system and environment factors. The paper describes the results obtained applying this framework to a basic goal-directed guidance task taking place in an urban environment. Three small-scale UAV types are compared: a fixed-wing aircraft, a standard helicopter, and a quad-rotorcraft. Both minimum-time and minimum-energy performance criteria are analyzed to determine overall performance characteristics and highlight the basic features of the proposed framework.
|Original language||English (US)|
|Number of pages||22|
|Journal||Journal of Intelligent and Robotic Systems: Theory and Applications|
|State||Published - Jan 2011|
Bibliographical noteFunding Information:
Acknowledgement This research work was enabled thanks to the financial support of NASA Ames (Grant Number NNX07AN31A).
- Autonomous guidance
- Performance evaluation
- Spatial cost-to-go function
- Trajectory optimization