Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
Learning skills from demonstrations: A trend from motion primitives to experience abstraction
The uses of robots are changing from static environments in factories to encompass novel
concepts such as human–robot collaboration in unstructured settings. Preprogramming all …
concepts such as human–robot collaboration in unstructured settings. Preprogramming all …
Adaptive safe reinforcement learning with full-state constraints and constrained adaptation for autonomous vehicles
High-performance learning-based control for the typical safety-critical autonomous vehicles
invariably requires that the full-state variables are constrained within the safety region even …
invariably requires that the full-state variables are constrained within the safety region even …
Efficient reliability-based path planning of off-road autonomous ground vehicles through the coupling of surrogate modeling and RRT
Reliability-based global path planning incorporates reliability constraints into path planning
to ensure that off-road autonomous ground vehicles can operate reliably in uncertain off …
to ensure that off-road autonomous ground vehicles can operate reliably in uncertain off …
An efficient spatial-temporal trajectory planner for autonomous vehicles in unstructured environments
As a fundamental component of autonomous driving systems, motion planning has garnered
significant attention from both academia and industry. This paper focuses on efficient and …
significant attention from both academia and industry. This paper focuses on efficient and …
Decentralized iLQR for cooperative trajectory planning of connected autonomous vehicles via dual consensus ADMM
Cooperative trajectory planning of connected autonomous vehicles (CAVs) generally admits
strong nonlinearity and non-convexity, rendering great difficulties in finding the optimal …
strong nonlinearity and non-convexity, rendering great difficulties in finding the optimal …
Trajectory optimization of wall-building robots using response surface and non-dominated sorting genetic algorithm III
Q Shi, Z Wang, X Ke, Z Zheng, Z Zhou, Z Wang… - Automation in …, 2023 - Elsevier
Traditional wall-building robots regard brick masonry as a simple assembly process,
ignoring the viscoelastic effect of cement mortar, which leads to poor masonry quality …
ignoring the viscoelastic effect of cement mortar, which leads to poor masonry quality …
Hierarchical distribution-based tightly-coupled LiDAR inertial odometry
C Wang, Z Cao, J Li, J Yu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
LiDAR inertial odometry (LIO) has attracted much attention due to the complementarity of
LiDAR and IMU measurements. In the distribution-based LIO, the components related to …
LiDAR and IMU measurements. In the distribution-based LIO, the components related to …
A Comprehensive Survey and Tutorial on Smart Vehicles: Emerging Technologies, Security Issues, and Solutions Using Machine Learning
According to research, the vast majority of road accidents (90%) are the result of human
error, with only a small percentage (2%) being caused by malfunctions in the vehicle. Smart …
error, with only a small percentage (2%) being caused by malfunctions in the vehicle. Smart …
End-to-end autonomous navigation based on deep reinforcement learning with a survival penalty function
SL Jeng, C Chiang - Sensors, 2023 - mdpi.com
An end-to-end approach to autonomous navigation that is based on deep reinforcement
learning (DRL) with a survival penalty function is proposed in this paper. Two actor–critic …
learning (DRL) with a survival penalty function is proposed in this paper. Two actor–critic …