Risk based control in uncertain environments

Controllers for dynamic robots which regulate around an optimized trajectory often struggle with reliability in uncertain environments, and making control decisions in real-time. Using a map of risk to the robot's state, a risk gradient controller can be created to find paths back to safety from anywhere in the robot's reachable space. Despite generating a variety of complex and dynamic behaviors, this method suffers from sensitivity to sensor and process noise, as the value of risk associated with the robot's state is not well behaved. In order to correct for this sensitivity, a Stochastic Risk Gradient Controller (RGC) and sample-based Extended Kalman Filter (EKF) are proposed. The Sample Based EKF uses pre-simulated dynamics to generate optimal state and uncertainty estimate up to 10 times faster than an online-simulation. The Stochastic RGC then uses those estimates to calculate more robust control actions in real-time. This framework is applied to a model of a pogo-stick robot and simulated with various levels of sensor and process noise. In trials with relatively large measurement noise, the Stochastic Risk Gradient controller succeeded up to 15% more often than the naive risk gradient controller, while failing up to 8% more often in cases when measurement noise was relatively low.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Roudebush, George Imre.

Advisor dc:contributor.advisor

• Sangbae Kim.

• Mechanical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145221

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145221