Engineering topics
Barton J Bacon
Publications and source records attributed to Barton J Bacon.
Intelligent Contingency Management for Urban Air Mobility
The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithms together to assess vehicle capabilities, project these into the future and make decisions on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicles before the final state of autonomy is reached.
High-Alpha Handling Qualities Flight Research on the NASA F/A-18 High Alpha Research Vehicle
A flight research study of high-angle-of-attack handling qualities has been conducted at the NASA Dryden Flight Research Center using the F/A-18 High Alpha Research Vehicle (HARV). The objectives were to create a high-angle-of-attack handling qualities flight database, develop appropriate research evaluation maneuvers, and evaluate high-angle-of-attack handling qualities guidelines and criteria. Using linear and nonlinear simulations and flight research data, the predictions from each criterion were compared with the pilot ratings and comments. Proposed high-angle-of-attack nonlinear design guidelines and proposed handling qualities criteria and guidelines developed using piloted simulation were considered. Recently formulated time-domain Neal-Smith guidelines were also considered for application to high-angle-of-attack maneuvering. Conventional envelope criteria were evaluated for possible extension to the high-angle-of-attack regime. Additionally, the maneuvers were studied as potential evaluation techniques, including a limited validation of the proposed standard evaluation maneuver set. This paper gives an overview of these research objectives through examples and summarizes result highlights. The maneuver development is described briefly, the criteria evaluation is emphasized with example results given, and a brief discussion of the database form and content is presented.
Intelligent Contingency Management for Urban Air Mobility
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Intelligent Contingency Management for Urban Air Mobility
The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. This paper proposes an ICM architecture with associated tools that would help enable the UAM vision.
Intelligent Contingency Management for Urban Air Mobility
The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. This paper proposes an ICM architecture with associated tools that would help enable the UAM vision.
Collision Avoidance Approach Using Deep Reinforcement Learning
A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.