223 citations · 516 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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 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…