This paper describes the design and flight test of a completely self-contained autonomous indoor miniature unmanned aerial system (M-UAS). Guidance, navigation, and control algorithms are presented, enabling the M-UAS to autonomously explore cluttered indoor areas without relying on any off-board computation or external navigation aids such as Global Positioning Satellite (GPS). The system uses a scanning laser rangefinder and a streamlined simultaneous localization and mapping (SLAM) algorithm to provide a position and heading estimate, which is combined with other sensor data to form a six-degree-of-freedom inertial navigation solution. This enables an accurate estimate of the vehicle attitude, relative position, and velocity. The state information, with a selfgenerated map, is used to implement a frontier-based exhaustive search of an indoor environment. Improvements to existing guidance algorithms balance exploration with the need to remain within sensor range of indoor structures such that the SLAM algorithm has sufficient information to form a reliable position estimate. A dilution of the precision metric is developed to quantify the effect of environment geometry on the SLAM pose covariance, which is then used to update the two-dimensional position and heading in the navigation filter. Simulation and flight-test results validate the presented algorithms.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Aerospace Engineering
- Space and Planetary Science
- Electrical and Electronic Engineering
- Applied Mathematics