Ornithopter Trajectory Optimization with Neural Networks and Random Forest
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Ornithopter Trajectory Optimization with Neural Networks and Random Forest
Authors
Keywords
-
Journal
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
Volume 105, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-05-07
DOI
10.1007/s10846-022-01612-5
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A novel deep neural network architecture for real-time water demand forecasting
- (2021) Tony Salloom et al. JOURNAL OF HYDROLOGY
- Predicting energy cost of public buildings by artificial neural networks, CART, and random forest
- (2021) Marijana Zekić-Sušac et al. NEUROCOMPUTING
- Kinodynamic planning for an energy-efficient autonomous ornithopter
- (2021) Fabio Rodríguez et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction
- (2021) Tony Salloom et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Autonomous Landing of Rotary Wing Unmanned Aerial Vehicles on Underway Ships in a Sea State
- (2021) Jordan Ross et al. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
- Adaptive Fuzzy Full-State and Output-Feedback Control for Uncertain Robots With Output Constraint
- (2020) Xinbo Yu et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Adaptive Neural Network Control of Underwater Robotic Manipulators Tuned by a Genetic Algorithm
- (2019) Tony Salloom et al. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
- Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil
- (2019) Guilherme Garcia de Oliveira et al. NATURAL HAZARDS
- The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review
- (2018) Walton Pereira Coutinho et al. COMPUTERS & INDUSTRIAL ENGINEERING
- A study on the optimal route design considering time of mobile robot using recurrent neural network and reinforcement learning
- (2018) Min Hyuk Woo et al. Journal of Mechanical Science and Technology
- An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation
- (2017) Matthew Kelly SIAM REVIEW
- Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery
- (2017) Te Han et al. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
- A novel path planning method for biomimetic robot based on deep learning
- (2016) Yang Lu et al. ASSEMBLY AUTOMATION
- Cooperative and Geometric Learning Algorithm (CGLA) for path planning of UAVs with limited information
- (2014) Baochang Zhang et al. AUTOMATICA
- Optimal online trajectory generation for a flying robot for terrain following purposes using neural network
- (2014) Amirreza Kosari et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING
- C-FOREST: Parallel Shortest Path Planning With Superlinear Speedup
- (2013) Michael Otte et al. IEEE Transactions on Robotics
- Geometric Reinforcement Learning for Path Planning of UAVs
- (2013) Baochang Zhang et al. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
- Discovery of complex behaviors through contact-invariant optimization
- (2012) Igor Mordatch et al. ACM TRANSACTIONS ON GRAPHICS
- Neural Network-Based Trajectory Optimization for Unmanned Aerial Vehicles
- (2012) Joseph F. Horn et al. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
- Beyond Feedforward Models Trained by Backpropagation: A Practical Training Tool for a More Efficient Universal Approximator
- (2008) R. Ilin et al. IEEE TRANSACTIONS ON NEURAL NETWORKS
- Designing a Biomimetic Ornithopter Capable of Sustained and Controlled Flight
- (2008) Joon Hyuk Park et al. Journal of Bionic Engineering
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExplorePublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More