Neural Networks for Path Planning
Salim Janji, Adrian Kliks
- Year
- 2022
- Access
- Open access
Abstract
The scientific community is able to present a new set of solutions to practical problems that substantially improve the performance of modern technology in terms of efficiency and speed of computation due to the advancement in neural networks architectures. We present the latest works considering the utilization of neural networks in robot path planning. Our survey shows the contrast between different formulations of the problems that consider different inputs, outputs, and environments and how different neural networks architectures are able to provide solutions to all of the presented problems.
Keywords
Related papers
Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
Keyi Shen, Glen Chou
2026
Artificial Intelligence enhanced smart welding islands: Foundation models revolutionizing manufacturing
Xiwei Wu, Wei Wu, Qiqi Chen +6 more
Robotics and Computer-Integrated Manufacturing · 2026
A deep reinforcement learning and a dynamic graph neural network-based scheduling agent to control a multi-task robot
Hedi Boukamcha, Anas Neumann, Monia Rekik +3 more
Robotics and Computer-Integrated Manufacturing · 2026
LLM Agent-driven Automated DFA Assessment with Fine-tuning and AAS-based RAG
Jiaxin Liu, Xiaofeng Zhou, Suyang Yu +5 more
Robotics and Computer-Integrated Manufacturing · 2026