Neural Networks for Path Planning
- URL: http://arxiv.org/abs/2207.00874v1
- Date: Sat, 2 Jul 2022 16:13:13 GMT
- Title: Neural Networks for Path Planning
- Authors: Salim Janji and Adrian Kliks
- Abstract summary: 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.
- Score: 0.24366811507669117
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
Related papers
- Neural Networks for Vehicle Routing Problem [0.0]
Route optimization can be viewed as a new challenge for neural networks.
Recent developments in machine learning provide a new toolset, for tackling complex problems.
The main area of application of neural networks is the area of classification and regression.
arXiv Detail & Related papers (2024-09-17T15:45:30Z) - Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
We propose to represent neural networks as computational graphs of parameters.
Our approach enables a single model to encode neural computational graphs with diverse architectures.
We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
arXiv Detail & Related papers (2024-03-18T18:01:01Z) - Reasoning Algorithmically in Graph Neural Networks [1.8130068086063336]
We aim to integrate the structured and rule-based reasoning of algorithms with adaptive learning capabilities of neural networks.
This dissertation provides theoretical and practical contributions to this area of research.
arXiv Detail & Related papers (2024-02-21T12:16:51Z) - Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences.
It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations.
Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
arXiv Detail & Related papers (2024-02-20T15:23:24Z) - An Analysis of Physics-Informed Neural Networks [0.0]
We present a new approach to approximating the solution to physical systems - physics-informed neural networks.
The concept of artificial neural networks is introduced, the objective function is defined, and optimisation strategies are discussed.
The partial differential equation is then included as a constraint in the loss function for the problem, giving the network access to knowledge of the dynamics of the physical system it is modelling.
arXiv Detail & Related papers (2023-03-06T04:45:53Z) - Multiobjective Evolutionary Pruning of Deep Neural Networks with
Transfer Learning for improving their Performance and Robustness [15.29595828816055]
This work proposes MO-EvoPruneDeepTL, a multi-objective evolutionary pruning algorithm.
We use Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm.
Experiments show that our proposal achieves promising results in all the objectives, and direct relation are presented.
arXiv Detail & Related papers (2023-02-20T19:33:38Z) - Physics informed neural networks for continuum micromechanics [68.8204255655161]
Recently, physics informed neural networks have successfully been applied to a broad variety of problems in applied mathematics and engineering.
Due to the global approximation, physics informed neural networks have difficulties in displaying localized effects and strong non-linear solutions by optimization.
It is shown, that the domain decomposition approach is able to accurately resolve nonlinear stress, displacement and energy fields in heterogeneous microstructures obtained from real-world $mu$CT-scans.
arXiv Detail & Related papers (2021-10-14T14:05:19Z) - A deep learning theory for neural networks grounded in physics [2.132096006921048]
We argue that building large, fast and efficient neural networks on neuromorphic architectures requires rethinking the algorithms to implement and train them.
Our framework applies to a very broad class of models, namely systems whose state or dynamics are described by variational equations.
arXiv Detail & Related papers (2021-03-18T02:12:48Z) - NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural
Network Synthesis [53.106414896248246]
We present a framework that allows analysts to effectively build the solution sub-graph space and guide the network search by injecting their domain knowledge.
Applying this technique in an iterative manner allows analysts to converge to the best performing neural network architecture for a given application.
arXiv Detail & Related papers (2020-09-28T01:48:45Z) - Spiking Neural Networks Hardware Implementations and Challenges: a
Survey [53.429871539789445]
Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles.
We present the state of the art of hardware implementations of spiking neural networks.
We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
arXiv Detail & Related papers (2020-05-04T13:24:00Z) - Binary Neural Networks: A Survey [126.67799882857656]
The binary neural network serves as a promising technique for deploying deep models on resource-limited devices.
The binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network.
We present a survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error.
arXiv Detail & Related papers (2020-03-31T16:47:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.