Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Electrical & Systems Engineering

First Advisor

Rahul Mangharam


Safe autonomous operation of dynamical systems has become one of the most important

research problems. Algorithms for planning and control of such systems are now

nding place on production vehicles, and are fast becoming ubiquitous on the roads

and air-spaces. However most algorithms for such operations, that provide guarantees,

either do not scale well or rely on over-simplifying abstractions that make them

impractical for real world implementations. On the other hand, the algorithms that

are computationally tractable and amenable to implementation generally lack any

guarantees on their behavior.

In this work, we aim to bridge the gap between provable and scalable planning

and control for dynamical systems. The research covered herein can be broadly

categorized into: i) multi-agent planning with temporal logic specications, and ii)

robust predictive control that takes into account the performance of the perception

algorithms used to process information for control.

In the rst part, we focus on multi-robot systems with complicated mission requirements,

and develop a planning algorithm that can take into account a) spatial,

b) temporal and c) reactive mission requirements across multiple robots. The algorithm

not only guarantees continuous time satisfaction of the mission requirements,

but also that the generated trajectories can be followed by the robot.

The other part develops a robust, predictive control algorithm to control the

the dynamical system to follow the trajectories generated by the rst part, within

some desired bounds. This relies on a contract-based framework wherein the control

algorithm controls the dynamical system as well as a resource/quality trade-o in a

perception-based state estimation algorithm. We show that this predictive algorithm

remains feasible with respect to constraints while following a desired trajectory, and

also stabilizes the dynamical system under control.

Through simulations, as well as experiments on actual robotic systems, we show

that the planning method is computationally ecient as well as scales better than

other state-of-the art algorithms that use similar formal specications. We also show

that the robust control algorithm provides better control performance, and is also

computationally more ecient than similar algorithms that do not leverage the resource/

quality trade-o of the perception-based state estimator

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