Autonomous Multi-Agent Systems and UAV Technologies: Trends and Innovations

The recent advancements in multi-agent systems and UAV technologies have significantly pushed the boundaries of autonomous coordination and control in various applications. A notable trend is the integration of deep reinforcement learning (DRL) to optimize task scheduling and trajectory planning in complex, high-dimensional environments. This approach allows for scalable solutions that can adapt to varying numbers of agents, enhancing operational efficiency and reducing human intervention. Additionally, the development of nature-inspired algorithms for collision-avoidance and formation control in 3D spaces has shown promise in urban and aerospace scenarios. The emphasis on real-time systems and middleware for seamless execution of high-level scheduling algorithms further bridges the gap between theoretical advancements and practical deployment. Notably, the use of data compression in mobile edge computing to optimize energy consumption and task offloading has demonstrated significant efficiency gains. Overall, the field is moving towards more autonomous, scalable, and energy-efficient solutions, with a strong focus on real-world applicability and robustness.

Sources

Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network

Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection

Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks

A Real-Time System for Scheduling and Managing UAV Delivery in Urban

Swarm Intelligence in Collision-free Formation Control for Multi-UAV Systems with 3D Obstacle Avoidance Maneuvers

Multi-UAV Collaborative Trajectory Planning for Seamless Data Collection and Transmission

A MARL Based Multi-Target Tracking Algorithm Under Jamming Against Radar

Distributed satellite information networks: Architecture, enabling technologies, and trends

Physical simulation of Marsupial UAV-UGV Systems Connected by a Hanging Tether using Gazebo

Flight Patterns for Swarms of Drones

Robust UAV Jittering and Task Scheduling in Mobile Edge Computing with Data Compression

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