Motion planning for mobile robots—Focusing on deep reinforcement learning: A systematic review
H Sun, W Zhang, R Yu, Y Zhang - IEEE Access, 2021 - ieeexplore.ieee.org
Mobile robots contributed significantly to the intelligent development of human society, and
the motion-planning policy is critical for mobile robots. This paper reviews the methods …
the motion-planning policy is critical for mobile robots. This paper reviews the methods …
A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
Deep reinforcement learning for indoor mobile robot path planning
This paper proposes a novel incremental training mode to address the problem of Deep
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate …
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate …
Modified Q-learning with distance metric and virtual target on path planning of mobile robot
Path planning is an essential element in mobile robot navigation. One of the popular path
planners is Q-learning–a type of reinforcement learning that learns with little or no prior …
planners is Q-learning–a type of reinforcement learning that learns with little or no prior …
Long-range indoor navigation with PRM-RL
Long-range indoor navigation requires guiding robots with noisy sensors and controls
through cluttered environments along paths that span a variety of buildings. We achieve this …
through cluttered environments along paths that span a variety of buildings. We achieve this …
A novel direct trajectory planning approach based on generative adversarial networks and rapidly-exploring random tree
Trajectory planning is essential for self-driving vehicles and has stringent requirements for
accuracy and efficiency. The existing trajectory planning methods have limitations in the …
accuracy and efficiency. The existing trajectory planning methods have limitations in the …
Receding-horizon reinforcement learning approach for kinodynamic motion planning of autonomous vehicles
X Zhang, Y Jiang, Y Lu, X Xu - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Kinodynamic motion planning is critical for autonomous vehicles with high maneuverability
in dynamic environments. However, obtaining near-optimal motion planning solutions with …
in dynamic environments. However, obtaining near-optimal motion planning solutions with …
Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies
In this paper we present the first safe system for full control of self-driving vehicles trained
from human demonstrations and deployed in challenging, real-world, urban environments …
from human demonstrations and deployed in challenging, real-world, urban environments …
Learning inverse kinodynamics for accurate high-speed off-road navigation on unstructured terrain
This letter presents a learning-based approach to consider the effect of unobservable world
states in kinodynamic motion planning in order to enable accurate high-speed off-road …
states in kinodynamic motion planning in order to enable accurate high-speed off-road …
A general framework of motion planning for redundant robot manipulator based on deep reinforcement learning
X Li, H Liu, M Dong - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
Motion planning and its optimization is vital and difficult for redundant robot manipulator in
an environment with obstacles. In this article, a general motion planning framework that …
an environment with obstacles. In this article, a general motion planning framework that …