Online Slip Prediction for Mobile Robots 16831 Project Proposal - - PowerPoint PPT Presentation

Online Slip Prediction for Mobile Robots

16831 Project Proposal Neal Seegmiller, Chris Skonieczny

Prior Work

Rogers-Marcovitz, Forrest. “On-line Mobile Robotic Dynamic Modeling using Integrated Perturbative Dynamics,” Master's Thesis, tech. report CMU-RI-TR-10- 15, Robotics Institute, Carnegie Mellon University, May, 2010

• Learn dynamic model for a mobile robot that accounts for wheel slip.
• “Integrated”:
• Most approaches are parameterized:
• Vehicle Ground Model Identification Project. Future goals include perception,

learning different models for multiple terrain types

16831 Project Objectives

Least Square Regression Gaussian Process Regression Bayes Linear Regression

Objectives: 1. Make online results better match offline batch results 2. Accurately quantify uncertainty

OFFLINE (assume constant speed and curvature commands) ONLINE (transient commands allowed) Extended Kalman Filter

Evaluation metrics

• Things quickly forgotten cannot be said to have truly

been learned

• Two important metrics that capture this:

– Proximity of online solution to an offline approach that treats all data points equally – Stability of parameter estimates

Potential online parameter learning approaches

• Online Bayes Linear Regression
• Re-tuned Extended Kalman Filter

– Reduce high weighting of recent data relative to older data

• Markov Random Field

– State: slip for a bin of commanded speed and curvature – Measurement: difference between measured and predicted slip

• Other suggestions?

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