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Found 8 papers, 6 papers with code
Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest.
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms.
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs).
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks
Deep Reinforcement Learning (DRL) is gaining attention as a potential approach to design trajectories for autonomous unmanned aerial vehicles (UAV) used as flying access points in the context of cellular or Internet of Things (IoT) connectivity.
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning methods.
UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission.
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods.
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest.
Robotics Systems and Control Systems and Control
