Multi-rotor aircraft collision avoidance using partially observable Markov decision processes
Abstract/Contents
- Abstract
- This dissertation presents an extension of the ACAS X collision avoidance algorithm to multi-rotor aircraft capable of using speed changes to avoid close encounters with neighboring aircraft. The ACAS X family of algorithms currently use either turns or vertical maneuvers to avoid collision. I present a formulation of the algorithm in two dimensions that uses horizontal accelerations for resolution maneuvers and propose a set of optimization metrics that directly specify aircraft behavior in terms of separation from other aircraft and deviation from the desired trajectory. The maneuver strategy is optimized with respect to a partially observable Markov decision process model using dynamic programming. The parameters of the model strongly influence the performance tradeoff between metrics such as alert rate and safety. Finding the parameters that provide the appropriate tradeoff was aided by a Gaussian process-based surrogate model. Sets of algorithm parameters were generated that provide a tradeoff between the two goals. These parameter sets allow a user of the collision avoidance algorithm to select a desired separation distance appropriate for their application that also minimizes trajectory deviations. Three additional collision avoidance algorithms were developed for comparison with the partially observable Markov decision process formulation. The first comparison algorithm is based on a potential field method. The second is an adaptation of a tactical conflict detection and resolution algorithm that uses candidate trajectory predictions to determine a preferred resolution. The third is based on receding-horizon model predictive control. The four algorithms are evaluated under a common set of assumptions, simulation capabilities and metrics. A batch simulation system generates individual trajectory and aggregate metrics related to each algorithm's performance, allowing direct comparison of the benefits and drawbacks of each approach. The first encounter model of hobbyist unmanned aircraft trajectories is presented and used to generate trajectories that have more realistic intruder accelerations than prior methods of simulating such aircraft. All algorithms are shown to have the flexibility to provide different tradeoffs between separation from an intruder and the trajectory deviation necessary to achieve that separation. The ACAS X extension algorithm delivers maximum deviation performance equivalent to the best alternative, the model predictive control algorithm, with only slightly smaller separations, and it does so with less than half of the required velocity change. Proposed extensions of this algorithm may allow it to surpass the others both in terms of collision avoidance performance and suitability for real-world deployment and certification. This research shows that it is feasible to formulate the collision avoidance problem for multi-rotor aircraft as a partially observable Markov decision process and that its performance across multiple metrics can equal, and even surpass, alternative approaches.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Mueller, Eric R |
|---|---|
| Associated with | Stanford University, Department of Aeronautics and Astronautics. |
| Primary advisor | Kochenderfer, Mykel J, 1980- |
| Thesis advisor | Kochenderfer, Mykel J, 1980- |
| Thesis advisor | Alonso, Juan José, 1968- |
| Thesis advisor | Erzberger, Heinz |
| Thesis advisor | Rock, Stephen M |
| Advisor | Alonso, Juan José, 1968- |
| Advisor | Erzberger, Heinz |
| Advisor | Rock, Stephen M |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Eric R. Mueller. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Eric Richard Mueller
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...