Date of Award
Doctor of Philosophy (PhD)
Electrical & Systems Engineering
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
Pant, Yash Vardhan, "Safe Planning And Control Of Autonomous Systems: Robust Predictive Algorithms" (2019). Publicly Accessible Penn Dissertations. 3419.