Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing.
Ramón GonzálezMirko FiacchiniKarl IagnemmaPublished in: Robotics Auton. Syst. (2018)
Keyphrases
- mobile robot
- machine learning
- conformal prediction
- unstructured environments
- robotic systems
- sensor fusion
- regression model
- model selection
- survival analysis
- path planning
- prediction model
- machine learning methods
- prediction accuracy
- classification and regression problems
- support vector regression
- prediction algorithm
- pattern recognition
- mobile robotics
- mercer kernels
- learning algorithm
- decision trees
- multi robot
- autonomous navigation
- predictive modeling
- predictive clustering trees
- motion control
- real time
- artificial intelligence
- support vector machine
- machine learning algorithms
- dynamic environments
- knowledge acquisition
- text classification
- text mining
- data mining
- feature selection
- multiple linear regression
- information extraction
- ridge regression
- unknown environments
- sensor networks
- least squares
- linear regression
- collision avoidance
- radial basis function network
- obstacle avoidance
- autonomous robots
- indoor environments
- prediction error