Collision Avoidance for Unmanned Aircraft using Markov Decision Processes

By S. Temizer, Mykel J. Kochenderfer, Leslie Pack Kaelbling, Tomas Lozano-Perez, J. Kuchar. 2010

151 citations recorded by the source corpus.

we investigate the automatic generation of collision avoidance algorithms given models of aircraft dynamics, sensor performance, and intruder behavior. By formulating the problem of collision avoidance as a Markov Decision Process (MDP) for sensors that provide precise localization of the intruder aircraft, or a Partially Observable Markov Decision Process (POMDP) for sensors that have positional uncertainty or limited eld-of-view constraints, generic MDP/POMDP solvers can be used to generate avoidance strategies that optimize a cost function that balances ight-plan deviation with collision. Experimental results demonstrate the suitability of such an approach using four dierent sensor modalities and a parametric aircraft performance model.

Subfields: Aerospace Engineering, Artificial Intelligence, Automotive Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Charles Richter
2. Nicholas A. Roy
3. William Vega-Brown
4. Markus Achtelik
5. Vijay R. Kumar

In Artificial Intelligence

1. Osbert Bastani
2. Charles Richter
3. Carolyn Kim
4. Nicholas A. Roy
5. William Vega-Brown
6. Hamsa Bastani
7. Carolyn Kim
8. D. V. Carvalho

In Automotive Engineering

1. Nicholas A. Roy

Citations in the corpus

• Interpretability via Model Extraction (2017, background): “Recent advances in machine learning have revolutionized our ability to use data to inform critical decisions, such as medical diagnosis [8, 19, 27], bail decisions for defendants [16, 17], and aircra‰ collision avoidance systems [25].”
• Interpreting Blackbox Models via Model Extraction (2017, background): “Recent advances in machine learning have revolutionized our ability to use data to inform critical decisions, such as medical diagnosis [30, 16, 44], bail decisions for defendants [28, 27] and the design of aircraft collision avoidance systems [42].”
• Bayesian Learning for Safe High-Speed Navigation in Unknown Environments (2015, methodology): “POMDPs have been used for aircraft collision avoidance [27, 5], where a very large negative reward discourages collisions at nearly any cost.”
• Machine Learning Interpretability: A Survey on Methods and Metrics (2019, background): “The latter refers to well-studied systems that have been used for a while, including postal code sorting and aircraft collision systems [71], which compute their output without human intervention.”