Robust execution for stochastic hybrid systems

Unmanned systems, such as Autonomous Underwater Vehicles (AUVs), planetary rovers and space probes, have enormous potential in areas such as reconnaissance and space exploration. However the effectiveness and robustness of these systems is currently restricted by a lack of autonomy. Previous work introduced the concept of a model-based executive, which increases the level of autonomy, elevating the level at which systems are commanded. This simplifies the operator's task and leaves degrees of freedom in the plan that allow the executive to optimize resources and ensure robustness to uncertainty. Uncertainty arises due to uncertain state estimation, disturbances, model uncertainty and component failures. This thesis develops a model-based executive that reasons explicitly from a stochastic hybrid discrete-continuous system model to find the optimal course of action, while ensuring the required level of robustness to uncertainty is achieved. Our first contribution is a novel 'Particle Control' approach for robust execution of state plans with stochastic hybrid systems. We introduce the notion of chance-constrained state plan execution; this means that the executive ensures tasks in the state plan have at least a specified minimum probability of success. The minimum probabilities are specified by the operator, enabling conservatism to be traded against performance. In order to make optimal chance-constrained execution tractable, the Particle Control approach approximates the system's state distribution using samples or 'particles' and optimizes the evolution of these particles to achieve chance-constrained state plan execution. In this manner particle control solves a tractable deterministic approximation to the original stochastic problem; furthermore the approximation becomes exact as the number of particles approaches infinity.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Blackmore, Lars James Christopher

Advisor dc:contributor.advisor

• Brian C. Williams.

• Aeronautics and Astronautics.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/42176

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/42176