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Collision avoidance for unmanned aircraft using Markov Decision Processes

August 2, 2010
  |
Conference Paper
Author:
Selim Temizer
…
Published in:
AIAA Guidance, Navigation, and Control Conf., 2-5 August 2010.
Topic:
collision avoidance
R&D area:
R&D group:

Summary

Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configuration, 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 field-of-view constraints, generic MDP/POMDP solvers can be used to generate avoidance strategies that optimize a cost function that balances flight-plan deviation with collision. Experimental results demonstrate the suitability of such an approach using four different sensor modalities and a parametric aircraft performance model.
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Summary

Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configuration, we investigate the automatic generation of collision avoidance algorithms given models of aircraft dynamics, sensor performance, and intruder...

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Collision avoidance for unmanned aircraft using Markov Decision Processes

Model-based optimization of airborne collision avoidance logic

January 26, 2010
  |
Project Report
Author:
Mykel J. Kochenderfer
…
Published in:
MIT Lincoln Laboratory Report ATC-360
Topic:
collision avoidance
R&D area:
R&D group:

Summary

The Traffic Alert and Collision Avoidance System (TCAS) is designed to reduce the risk of mid-air collisions by providing resolution advisories to pilots. The current version of the collision avoidance logic was hand-crafted over the course of many years and contains many parameters that have been tuned to varying extents and heuristic rules whose justification has been lost. Further development of the TCAS system is required to make the system compatible with next generation air traffic control procedures and surveillance systems that will reduce separation between aircraft. This report presents a decision-theoretic approach to optimizing the TCAS logic using probabilistic models of aircraft behavior and a cost metric that balances the cost of alerting with the cost of collision. Such an approach ahs the potential for meeting or exceeding the current safety level while lowering the false alert rate and simplifing the process of re-optimizing the logic in response to changes in the airspace and sensor capabilities.
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Summary

The Traffic Alert and Collision Avoidance System (TCAS) is designed to reduce the risk of mid-air collisions by providing resolution advisories to pilots. The current version of the collision avoidance logic was hand-crafted over the course of many years and contains many parameters that have been tuned to varying extents...

READ MORE
Model-based optimization of airborne collision avoidance logic

Unmanned aircraft collision avoidance using partially observable Markov decision processes

September 22, 2009
  |
Project Report
Author:
Mykel J. Kochenderfer
…
Published in:
MIT Lincoln Laboratory Report ATC-356
Topic:
collision avoidance
R&D area:

Summary

Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configuration, this project investigates the automatic generation of collision avoidance logic given models of aircraft dynamics, sensor performance, and intruder behavior. By formulating the problem of collision avoidance as a partially-observable Markov decision process (POMDP), a generic POMDP solver can be used to generate avoidance strategies that optimize a cost function that balances flight-plan deviation with collision. Experimental results demonstrate the suitability of such an approach using three different sensor modalities and two aircraft performance models.
READ LESS

Summary

Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configuration, this project investigates the automatic generation of collision avoidance logic given models of aircraft dynamics, sensor performance, and...

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Unmanned aircraft collision avoidance using partially observable Markov decision processes
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