Safe, autonomous operation in complex, cluttered environments is a critical challenge facing autonomous mobile systems. The research described in this paper was motivated by a particularly difficult example of autonomous mobility: flight of a small unmanned aerial vehicle through a forest. The focus was on enabling the three critical tasks that comprise flight: 1) maintaining controlled flight while avoiding collisions (aviate); 2) flying from a known start location to a known goal location (navigate); and 3) providing information about the environment - a map - to a human operator or other robots in the team (communicate). Presented here is a solution to the problem of estimating vehicle state (its position, orientation, and velocity) as well as the positions of obstacles or landmarks in the environment using only inertial measurements and bearings to landmarks. This is a highly nonlinear estimation problem, and standard estimation techniques such as the extended Kalman filter are prone to divergence in this application. In this paper a sigma-point Kalman filter is implemented, resulting in an estimator which is able to cope with the significant nonlinearities in the system equations and uncertainty in state estimates while remaining tractable for real-time operation. In addition, the issues of data association and landmark initialization are addressed. Estimator performance is examined through Monte Carlo simulations in two dimensions for scenarios involving unmanned aerial vehicle flight in cluttered environments. Simulations show that convergent, consistent estimates of vehicle state and obstacle positions can be obtained and that the estimates can be used by a trajectory planner to generate a path through a cluttered environment.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Aerospace Engineering
- Space and Planetary Science
- Electrical and Electronic Engineering
- Applied Mathematics