Deep learning for safe autonomous driving: Current challenges and future directions
Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning
Mobility in an effective and socially-compliant manner is an essential yet challenging task for
robots operating in crowded spaces. Recent works have shown the power of deep …
robots operating in crowded spaces. Recent works have shown the power of deep …
Motion planning among dynamic, decision-making agents with deep reinforcement learning
Robots that navigate among pedestrians use collision avoidance algorithms to enable safe
and efficient operation. Recent works present deep reinforcement learning as a framework …
and efficient operation. Recent works present deep reinforcement learning as a framework …
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios
Developing a safe and efficient collision-avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths with limited observation …
in the decentralized scenarios where each robot generates its paths with limited observation …
Towards optimally decentralized multi-robot collision avoidance via deep reinforcement learning
Developing a safe and efficient collision avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths without observing other …
in the decentralized scenarios where each robot generates its paths without observing other …
Deep learning in robotics: Survey on model structures and training strategies
The ever-increasing complexity of robot applications induces the need for methods to
approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of …
approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of …
Decentralized structural-rnn for robot crowd navigation with deep reinforcement learning
Safe and efficient navigation through human crowds is an essential capability for mobile
robots. Previous work on robot crowd navigation assumes that the dynamics of all agents …
robots. Previous work on robot crowd navigation assumes that the dynamics of all agents …
Proximal policy optimization with reciprocal velocity obstacle based collision avoidance path planning for multi-unmanned surface vehicles
D Xue, D Wu, AS Yamashita, Z Li - Ocean Engineering, 2023 - Elsevier
The challenge of solving the collision avoidance path planning problem lies in adaptively
selecting the optimal agent velocity in complex scenarios full of reciprocal obstacles. To …
selecting the optimal agent velocity in complex scenarios full of reciprocal obstacles. To …
Robot navigation in crowds by graph convolutional networks with attention learned from human gaze
Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task.
Previous work has shown the power of deep reinforcement learning frameworks to train …
Previous work has shown the power of deep reinforcement learning frameworks to train …
[HTML][HTML] Decentralized multi-robot collision avoidance: A systematic review from 2015 to 2021
An exploration task can be performed by a team of mobile robots more efficiently than
human counterparts. They can access and give live updates for hard-to-reach areas such as …
human counterparts. They can access and give live updates for hard-to-reach areas such as …