Adaptive Systems and Advanced Sensing in Autonomous Operations

The recent developments in the research area of autonomous systems and advanced air mobility (AAM) have shown significant advancements in several key areas. There is a notable shift towards more adaptive and contextually-aware human-robot interactions, particularly in urban public environments, where robots are required to navigate complex socio-technical dynamics. This trend is supported by innovative frameworks that enhance the resilience and robustness of multi-agent systems, enabling more efficient and coordinated operations, especially in perceptually degraded environments.

Another prominent direction is the integration of advanced sensing technologies with decentralized control strategies, which are being leveraged to improve the operational efficiency and safety of autonomous vehicles, including unmanned aerial vehicles (UAVs) and vertical take-off and landing (VTOL) aircraft. These advancements are not only enhancing the autonomous navigation capabilities but also addressing critical safety concerns through the use of vision-based systems and system-theoretic process analysis (STPA).

Furthermore, there is a growing emphasis on the development of scalable and decentralized reinforcement learning frameworks, which are being applied to optimize collective behavior in UAV swarms for tasks such as target localization and autonomous navigation. These frameworks are proving to be effective in complex and unknown environments, where traditional methods fall short.

In the realm of human-computer interaction and human-AI collaboration, there is a comprehensive exploration of how to design interfaces and systems that ensure safe and effective operations in AAM. This includes the use of immersive technologies and AI-assisted decision-making systems, which are being integrated into pilot training and air traffic management.

Noteworthy papers include one that proposes a novel perimeter-free regional traffic management strategy utilizing existing parking infrastructure, which demonstrates significant improvements in operational efficiency. Another notable contribution is the development of a scalable decentralized reinforcement learning framework for UAV target localization, which shows promising results in perceptually degraded environments. Additionally, the integration of vision systems with STPA for robust landing and take-off in VTOL aircraft addresses critical safety challenges, enhancing the reliability of autonomous systems.

Sources

A CAV-based perimeter-free regional traffic control strategy utilizing existing parking infrastructure

Robots in the Wild: Contextually-Adaptive Human-Robot Interactions in Urban Public Environments

VTD: Visual and Tactile Database for Driver State and Behavior Perception

AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations

A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO

Vision-Based Deep Reinforcement Learning of UAV Autonomous Navigation Using Privileged Information

Memory-Based Control with Event-Triggered Protocol for interval type-2 fuzzy network system under fading channel

Human-Computer Interaction and Human-AI Collaboration in Advanced Air Mobility: A Comprehensive Review

Event-Triggered Memory Control for Interval Type-2 Fuzzy Heterogeneous Multi-Agent Systems

Bayesian Data Augmentation and Training for Perception DNN in Autonomous Aerial Vehicles

Survey on Human-Vehicle Interactions and AI Collaboration for Optimal Decision-Making in Automated Driving

Verification and Validation of a Vision-Based Landing System for Autonomous VTOL Air Taxis

An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

Vision-based indoor localization of nano drones in controlled environment with its applications

Advancing Operational Efficiency: Airspace Users' Perspective on Trajectory-Based Operations

An Event-Triggered Framework for Trust-Mediated Human-Autonomy Interaction

Multi-Aircraft Scheduling Optimization in Urban Environments

Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft

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