Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks

• URL: http://arxiv.org/abs/2308.04958v1
• Date: Wed, 9 Aug 2023 13:44:35 GMT
• Title: Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks
• Authors: Marc W. Brittain, Luis E. Alvarez, Kara Breeden
• Abstract summary: We present a reinforcement learning framework to provide autonomous self-separation capabilities within AAM corridors. The problem is formulated as a Markov Decision Process and solved by developing a novel extension to the sample-efficient, off-policy soft actor-critic (SAC) algorithm. A comprehensive numerical study shows that the proposed framework can ensure safe and efficient separation of aircraft in high density, dynamic environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advanced Air Mobility (AAM) introduces a new, efficient mode of transportation with the use of vehicle autonomy and electrified aircraft to provide increasingly autonomous transportation between previously underserved markets. Safe and efficient navigation of low altitude aircraft through highly dense environments requires the integration of a multitude of complex observations, such as surveillance, knowledge of vehicle dynamics, and weather. The processing and reasoning on these observations pose challenges due to the various sources of uncertainty in the information while ensuring cooperation with a variable number of aircraft in the airspace. These challenges coupled with the requirement to make safety-critical decisions in real-time rule out the use of conventional separation assurance techniques. We present a decentralized reinforcement learning framework to provide autonomous self-separation capabilities within AAM corridors with the use of speed and vertical maneuvers. The problem is formulated as a Markov Decision Process and solved by developing a novel extension to the sample-efficient, off-policy soft actor-critic (SAC) algorithm. We introduce the use of attention networks for variable-length observation processing and a distributed computing architecture to achieve high training sample throughput as compared to existing approaches. A comprehensive numerical study shows that the proposed framework can ensure safe and efficient separation of aircraft in high density, dynamic environments with various sources of uncertainty.

Related papers

• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs [21.195346908715972]
  Unmanned aerial vehicles present an alternative means to offload data traffic from terrestrial BSs. This paper presents a novel approach to efficiently serve multiple UAVs for data offloading from terrestrial BSs.
  arXiv  Detail & Related papers  (2024-02-05T12:36:08Z)
• Toward collision-free trajectory for autonomous and pilot-controlled unmanned aerial vehicles [1.018017727755629]
  This study makes greater use of electronic conspicuity (EC) information made available by PilotAware Ltd in developing an advanced collision management methodology. The merits of the DACM methodology have been demonstrated through extensive simulations and real-world field tests in avoiding mid-air collisions.
  arXiv  Detail & Related papers  (2023-09-18T18:24:31Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning [9.891207216312937]
  Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility. We propose a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to guide autonomous UAS to their destinations. Results show that the proposed model can provide accurate and robust guidance and resolve conflict with a success rate of over 99%.
  arXiv  Detail & Related papers  (2021-11-13T04:44:53Z)
• Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications [69.24726496448713]
  We propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV. We show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.
  arXiv  Detail & Related papers  (2021-05-28T08:45:37Z)
• Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles [109.71532364079711]
  A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs) A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
  arXiv  Detail & Related papers  (2021-02-05T19:57:47Z)
• A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control [5.550794444001022]
  We propose a new intelligent decision making framework that leverages multi-agent reinforcement learning (MARL) to suggest adjustments of aircraft speeds in real-time. The goal of the system is to enhance the ability of an air traffic controller to provide effective guidance to aircraft to avoid air traffic congestion, near-miss situations, and to improve arrival timeliness.
  arXiv  Detail & Related papers  (2020-04-03T06:03:53Z)
• A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance [5.196149362684628]
  A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft. The proposed framework is validated on three challenging case studies in the BlueSky air traffic control environment.
  arXiv  Detail & Related papers  (2020-03-17T16:50:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.