223 citations · 516 across the 8 of their papers we have counts for
8 papers
Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Marco Caccamo +1
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. This work addresses the…
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Jichao Chen, Omid Esrafilian, Harald Bayerlein +2
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorit…
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
Raphael Trumpp, Harald Bayerlein, David Gesbert
Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where decis…
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks
Omid Esrafilian, Harald Bayerlein, David Gesbert
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…
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
Harald Bayerlein, Mirco Theile, Marco Caccamo +1
Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning…
UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Richard Nai +2
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission. This work presents a method for autonomous UAV path pl…
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
Harald Bayerlein, Mirco Theile, Marco Caccamo +1
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-e…
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Richard Nai +2
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. We propose a new method to control…