A Deep Reinforcement Learning Based Mapless Navigation Algorithm Using Continuous Actions

Duo, Nanxun; Wang, Qinzhao; Lu, Qiang; Wei, Heng; Zhang, Pei

doi:10.1109/icris.2019.00025

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…In recent years, deep RL (DRL), which combines RL and deep learning (DL), has emerged as an important research area in UAV control and decision-making. DRL alleviates the dimension explosion problem that easily occurs in traditional RL and has made great breakthroughs in areas such as robot control [21][22][23], scheduling optimization [24,25], and multi-agent collaboration [26][27][28][29]. Ma [30] proposed a task-assignment algorithm based on Deep Q Network (DQN) to support UAV swarm operations, which significantly improves the success rate of UAV swarm combat.…”

Section: Related Workmentioning

confidence: 99%

Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

Liu

Wang

et al. 2023

Drones

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UAV swarm applications are critical for the future, and their mission-planning and decision-making capabilities have a direct impact on their performance. However, creating a dynamic and scalable assignment algorithm that can be applied to various groups and tasks is a significant challenge. To address this issue, we propose the Extensible Multi-Agent Deep Deterministic Policy Gradient (Ex-MADDPG) algorithm, which builds on the MADDPG framework. The Ex-MADDPG algorithm improves the robustness and scalability of the assignment algorithm by incorporating local communication, mean simulation observation, a synchronous parameter-training mechanism, and a scalable multiple-decision mechanism. Our approach has been validated for effectiveness and scalability through both simulation experiments in the Multi-Agent Particle Environment (MPE) and a real-world experiment. Overall, our results demonstrate that the Ex-MADDPG algorithm is effective in handling various groups and tasks and can scale well as the swarm size increases. Therefore, our algorithm holds great promise for mission planning and decision-making in UAV swarm applications.

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Section: Related Workmentioning

confidence: 99%

Task Assignment of UAV Swarms Based on Deep Reinforcement Learning

Liu

Wang

et al. 2023

Drones

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“…The mobile robot mapless navigation problem [30], [31] is where a robot requires to move from an initial position to a desired goal without having prior knowledge of its environment and without the need to construct a map constantly. The previous statements are beneficial compared to mapbased navigation as it eliminates the time-consuming need to construct a map, and it reduces the computing resources needed in the robot to run a Simultaneous Localization and Mapping (SLAM) algorithm.…”

Section: B Mobile Robot Mapless Navigationmentioning

confidence: 99%

FRobs_RL: A Flexible Robotics Reinforcement Learning Library

Fajardo

Roldan

Realpe

et al. 2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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Reinforcement learning (RL) has become an interesting topic in robotics applications as it can solve complex problems in specific scenarios. The small amount of RL-tools focused on robotics, plus the lack of features such as easy transfer of simulated environments to real hardware, are obstacles to the widespread use of RL in robotic applications. FRobs RL is a Python library that aims to facilitate the implementation, testing, and deployment of RL algorithms in intelligent robotic applications using robot operating system (ROS), Gazebo, and OpenAI Gym. FRobs RL provides an Application Programming Interface (API) to simplify the creation of RL environments, where users can import a wide variety of robot models as well as different simulated environments. With the FRobs RL library, users do not need to be experts in ROS, Gym, or Gazebo to create a realistic RL application. Using the library, we created and tested two environments containing common robotic tasks; one is a reacher task using a robotic manipulator, and the other is a mapless navigation task using a mobile robot. The library is available in GitHub 1 .

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