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Position validation strategies using partially observable Markov decision processes

Published in:
Proc. 30th IEEE/AIAA Digital Avionics Systems Conference, DASC, 16-20 October 2011, pp. 4A2.

Summary

The collision avoidance system that is currently deployed worldwide relies upon radar beacon surveillance. With its broad deployment over the next decade, aviation surveillance based on Automatic Dependent Surveillance-Broadcast (ADS-B) reports may reduce the need for frequent beacon interrogation over the communication channel, but there is a risk of ADS-B providing erroneous data to the collision avoidance system, resulting in a potential collision. Hence, there is a need to use beacon interrogation to periodically validate ADS-B position reports. Various threshold-based validation strategies based on proximity and closure rate have been suggested to reduce channel congestion while maintaining the reliability of the collision avoidance system. This paper shows how to model the problem of deciding when to validate ADS-B reports as a partially observable Markov decision process, and it explains how to solve for the optimal validation strategy. The effectiveness of this approach is demonstrated in simulation.
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Summary

The collision avoidance system that is currently deployed worldwide relies upon radar beacon surveillance. With its broad deployment over the next decade, aviation surveillance based on Automatic Dependent Surveillance-Broadcast (ADS-B) reports may reduce the need for frequent beacon interrogation over the communication channel, but there is a risk of ADS-B...

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Analysis of open-loop and closed-loop planning for aircraft collision avoidance

Published in:
2011 14th Int. IEEE Conf. on Intelligent Transportation Systems, 5-7 October 2011, pp. 212-217.

Summary

Open-loop planning has been a popular approach for developing aircraft collision avoidance systems. Open-loop planning computes a future plan to follow without anticipation of how future observations can affect the future course of action. Closed-loop planning, in contrast, takes into account the ability to react to future information. This paper explores trade-offs that exist between the two strategies as they apply to aircraft collision avoidance. It demonstrates some of the performance gains that con be realized by adopting a closed-loop planning strategy.
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Summary

Open-loop planning has been a popular approach for developing aircraft collision avoidance systems. Open-loop planning computes a future plan to follow without anticipation of how future observations can affect the future course of action. Closed-loop planning, in contrast, takes into account the ability to react to future information. This paper...

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Hazard alerting based on probabilistic models

Published in:
AIAA Modeling and Simulation Technologies Conf., 8-11 August 2011.

Summary

Hazard alerting systems alert operators to potential future undesirable events so that action may be taken to mitigate risk. One way to develop a hazard alerting system based on probabilistic models is by using a threshold-based approach, where the probability of the undesirable event without mitigation is compared against a threshold. Another way to develop such a system is to model the system as a Markov decision process and solve for the hazard alerting strategy that maximizes expected utility. This paper analyzes and compares these two methods. The experiments reveal that an expected utility approach performs better than threshold-based approaches when the dynamic stochasticity is high, where accounting for delays or changes in the alert becomes more important. However, for certain system parameters and operating environments, a threshold-based approach may provide comparable performance.
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Summary

Hazard alerting systems alert operators to potential future undesirable events so that action may be taken to mitigate risk. One way to develop a hazard alerting system based on probabilistic models is by using a threshold-based approach, where the probability of the undesirable event without mitigation is compared against a...

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Accounting for state uncertainty in collision avoidance

Published in:
J. Guidance, Control, and Dynamics, Vol. 34, No. 4, July-August 2011, pp. 951-960.

Summary

An important consideration in the development of aircraft collision avoidance systems is how to account for state uncertainty due to sensor limitations and noise. However, many collision avoidance systems simply use point estimates of the state instead of leveraging the full posterior state distribution. Recently, there has been work on applying decision-theoretic methods to collision avoidance, but the importance of accommodating state uncertainty has not yet been well studied. This paper presents a computationally efficient framework for accounting for state uncertainty based on dynamic programming. Examination of characteristic encounters and Monte Carlo simulations demonstrates that properly handling state uncertainty rather than simply using point estimates can significantly enhance safety and improve robustness to sensor error.
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Summary

An important consideration in the development of aircraft collision avoidance systems is how to account for state uncertainty due to sensor limitations and noise. However, many collision avoidance systems simply use point estimates of the state instead of leveraging the full posterior state distribution. Recently, there has been work on...

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Collision avoidance system optimization with probabilistic pilot response models

Published in:
2011 American Control Conf., 29 June-1 July 2011, pp. 2765-2770.

Summary

All large transport aircraft are required to be equipped with a collision avoidance system that instructs pilots how to maneuver to avoid collision with other aircraft. Uncertainty in the compliance of pilots to advisories makes designing collision avoidance logic challenging. Prior work has investigated formulating the problem as a Markov decision process and solving for the optimal collision avoidance strategy using dynamic programming. The logic was optimized to a pilot response model in which the pilot responds deterministically to all alerts. Deviation from this model during flight can degrade safety. This paper extends the methodology to include probabilistic pilot response models that capture the variability in pilot behavior in order to enhance robustness.
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Summary

All large transport aircraft are required to be equipped with a collision avoidance system that instructs pilots how to maneuver to avoid collision with other aircraft. Uncertainty in the compliance of pilots to advisories makes designing collision avoidance logic challenging. Prior work has investigated formulating the problem as a Markov...

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Unmanned aircraft collision avoidance using continuous-state POMDPs

Published in:
2011 Robotics: Science and Systems, 27-30 June 2011.

Summary

An effective collision avoidance system for unmanned aircraft will enable them to fly in civil airspace and greatly expand their applications. One promising approach is to model aircraft collision avoidance as a partially observable Markov decision process (POMDP) and automatically generate the threat resolution logic for the collision avoidance system by solving the POMDP model. However, existing discrete-state POMDP algorithms cannot cope with the high-dimensional state space in collision avoidance POMDPs. Using a recently developed algorithm called Monte Carlo Value Iteration (MCVI), we constructed several continuous-state POMDP models and solved them directly, without discretizing the state space. Simulation results show that our 3-D continuous-state models reduce the collision risk by up to 70 times, compared with earlier 2-D discrete-state POMDP models. The success demonstrates both the benefits of continuous-state POMDP models for collision avoidance systems and the latest algorithmic progress in solving these complex models.
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Summary

An effective collision avoidance system for unmanned aircraft will enable them to fly in civil airspace and greatly expand their applications. One promising approach is to model aircraft collision avoidance as a partially observable Markov decision process (POMDP) and automatically generate the threat resolution logic for the collision avoidance system...

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A new approach for designing safer collision avoidance systems

Published in:
9th USA/Europe Air Traffic Management Research and Development Sem., ATM 2011, 14-17 June 2011.

Summary

The Traffic Alert and Collision Avoidance System (TCAS) has been shown to significantly reduce the risk of mid-air collision and is currently mandated worldwide on all large transport aircraft. Engineering the collision avoidance logic was a very costly undertaking that spanned several decades. The development followed an iterative process where the logic was specified using pseudocode, evaluated on encounters in simulation, and revised based on performance against a set of metrics. Modifying the logic to get the desired behavior is difficult because the pseudocode contains many heuristic rules that interact with each other in complex ways. Over the years, the TCAS logic has become challenging to maintain. With the anticipated introduction of next-generation air traffic control procedures and surveillance systems, the logic will require significant revision to prevent unnecessary alerts. Recent work has explored a new approach for designing collision avoidance systems that has the potential to shorten the development cycle, improve maintainability, and enhance safety with fewer false alerts. The approach involves leveraging recent advances in computation to automatically derive optimized collision avoidance logic directly from encounter models and performance metrics. This paper outlines the general approach and discusses the anticipated impact on development, safety, and operation.
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Summary

The Traffic Alert and Collision Avoidance System (TCAS) has been shown to significantly reduce the risk of mid-air collision and is currently mandated worldwide on all large transport aircraft. Engineering the collision avoidance logic was a very costly undertaking that spanned several decades. The development followed an iterative process where...

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Partially-controlled Markov decision processes for collision avoidance systems

Published in:
ICAART 2011, Proc. of the 2rd Int. Conf. on Agents and Artificial Intelligence, 28-30 January 2011, pp. 61-70.

Summary

Deciding when and how to avoid collision in stochastic environments requires accounting for the likelihood and relative costs of future sequences of outcomes in response to different sequences of actions. Prior work has investigated formulating the problem as a Markov decision process, discretizing the state space, and solving for the optimal strategy using dynamic programming. Experiments have shown that such an approach can be very effective, but scaling to higher-dimensional problems can be challenging due to the exponential growth of the discrete state space. This paper presents an approach that can greatly reduce the complexity of computing the optimal strategy in problems where only some of the dimensions of the problem are controllable. The approach is demonstrated on an airborne collision avoidance problem where the system must recommend maneuvers to an imperfect pilot.
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Summary

Deciding when and how to avoid collision in stochastic environments requires accounting for the likelihood and relative costs of future sequences of outcomes in response to different sequences of actions. Prior work has investigated formulating the problem as a Markov decision process, discretizing the state space, and solving for the...

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Aircraft collision avoidance using Monte Carlo real-time belief space search

Published in:
J. Intell. Robot. Syst., Vol. 64, No. 2, 2011, pp. 277-98.

Summary

The aircraft collision avoidance problem can be formulated using a decision-theoretic planning framework where the optimal behavior requires balancing the competing objectives of avoiding collision and adhering to a flight plan. Due to noise in the sensor measurements and the stochasticity of intruder state trajectories, a natural representation of the problem is as a partially-observable Markov decision process (POMDP), where the underlying state of the system is Markovian and the observations depend probabilistically on the state. Many algorithms for finding approximate solutions to POMDPs exist in the literature, but they typically require discretization of the state and observation spaces. This paper investigates the introduction of a sample-based representation of state uncertainty to an existing algorithm called Real-Time Belief Space Search (RTBSS), which leverages branch-and-bound pruning to make searching the belief space for the optimal action more efficient. The resulting algorithm, called Monte Carlo Real-Time Belief Space Search (MC-RTBSS), is demonstrated on encounter scenarios in simulation using a beacon-based surveillance system and a probabilistic intruder model derived from recorded radar data.
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Summary

The aircraft collision avoidance problem can be formulated using a decision-theoretic planning framework where the optimal behavior requires balancing the competing objectives of avoiding collision and adhering to a flight plan. Due to noise in the sensor measurements and the stochasticity of intruder state trajectories, a natural representation of the...

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Robust airborne collision avoidance through dynamic programming

Published in:
MIT Lincoln Laboratory Report ATC-371

Summary

The Traffic Alert and Collision Avoidance System (TCAS) uses an on-board beacon radar to monitor the local air traffic and logic to determine when to alert pilots to potential conflict. The current TCAS logic was the result of many years of development and involved the careful engineering of many heuristic rules specified in pseudocode. Unfortunately, due to the complexity of the logic, it is difficult to revise the pseudocode to accommodate the evolution of the airspace and the introduction of new technologies and procedures. This report summarizes recent advances in computational techniques for automatically deriving the optimal logic with respect to a probabilistic model and a set of performance metrics. Simulations demonstrate how this new approach results in logic that significantly outperforms TCAS according to the standard safety and operational performance metrics.
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Summary

The Traffic Alert and Collision Avoidance System (TCAS) uses an on-board beacon radar to monitor the local air traffic and logic to determine when to alert pilots to potential conflict. The current TCAS logic was the result of many years of development and involved the careful engineering of many heuristic...

READ MORE