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Python implementation of DDQN multi-UAV data harvesting
Energy-aware Multi-UAV Coverage Mission Planning with Optimal Speed of Flight
Library to solve Traveling Salesperson Problems with pure Python code
In this repository you can find the resuls of the simulated evaluation of an innovative, optimized for real-life use, STC-based, multi-robot Coverage Path Planning algorithm
Turn-minimizing Multi-robot Spanning Tree Coverage Path Planning
The repository contains code for Non-additive reward functions in Reinforcement Learning.
An Efficient Framework for Fast UAV Exploration
Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning
Informative Path Planning for Active Learning in Aerial Semantic Mapping
Classic Reinforcement Learning Methods for Coverage Path Planning. Results presented at ICARSC 2023
A Reinforcement Learning (RL) agent for Coverage Path Planning.
The Python implementation of the proposed framework in the paper Evolutionary Multi-Objective Deep Reinforcement Learning for Autonomous UAV Navigation in Large-Scale Complex Environments
Official implementation of the paper "Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning"
obstacle detection and avoidance for UAV via gazebo
This is a work on using meta-reinforcement learning to achieve autonomous target tracking and obstacle avoidance for UAVs
Reinforcement Learning-based exploration algorithm to drive a fleet of UAVs in an unknown environment.
A multi UAV test platform for area coverage path planning algorithms
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing
采用DDQN算法进行二维网格无人机的数据收集DH（多智能体）和区域覆盖CPP（单智能体）的算法，深度学习框架采用pytorch
GUI - Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning
The objectives is to maximize the coverage rate in a specific area.
An OpenaAIGym-based framework allowing to test hybrid approaches (RL + path planning) for multi-UAV systems that are supposed to provide smart services.
This is a new repo used for training UAV navigation (local path planning) policy using DRL methods.
An OpenAIGym-based framework allowing to test Delay-Aware Deep Reinforcement Learning algorithms for cooperative multi-UAV systems in fully customizable scenarios (e.g., coverage maximization, trac…