11,435 research outputs found
Safe Sequential Path Planning Under Disturbances and Imperfect Information
Multi-UAV systems are safety-critical, and guarantees must be made to ensure
no unsafe configurations occur. Hamilton-Jacobi (HJ) reachability is ideal for
analyzing such safety-critical systems; however, its direct application is
limited to small-scale systems of no more than two vehicles due to an
exponentially-scaling computational complexity. Previously, the sequential path
planning (SPP) method, which assigns strict priorities to vehicles, was
proposed; SPP allows multi-vehicle path planning to be done with a
linearly-scaling computational complexity. However, the previous formulation
assumed that there are no disturbances, and that every vehicle has perfect
knowledge of higher-priority vehicles' positions. In this paper, we make SPP
more practical by providing three different methods to account for disturbances
in dynamics and imperfect knowledge of higher-priority vehicles' states. Each
method has different assumptions about information sharing. We demonstrate our
proposed methods in simulations.Comment: American Control Conference, 201
Modeling rationality to control self-organization of crowds: An environmental approach
In this paper we propose a classification of crowd models in built
environments based on the assumed pedestrian ability to foresee the movements
of other walkers. At the same time, we introduce a new family of macroscopic
models, which make it possible to tune the degree of predictiveness (i.e.,
rationality) of the individuals. By means of these models we describe both the
natural behavior of pedestrians, i.e., their expected behavior according to
their real limited predictive ability, and a target behavior, i.e., a
particularly efficient behavior one would like them to assume (for, e.g.,
logistic or safety reasons). Then we tackle a challenging shape optimization
problem, which consists in controlling the environment in such a way that the
natural behavior is as close as possible to the target one, thereby inducing
pedestrians to behave more rationally than what they would naturally do. We
present numerical tests which elucidate the role of rational/predictive
abilities and show some promising results about the shape optimization problem
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Game-Theoretic Safety Assurance for Human-Centered Robotic Systems
In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence
Usable boundary for visibility-based surveillance-evasion games
We consider a surveillance-evasion game in an environment with obstacles. In
such an environment, a mobile pursuer seeks to maintain the visibility with a
mobile evader, who tries to get occluded from the pursuer in the shortest time
possible. In this two-player zero-sum game setting, we study the
discontinuities of the value of the game near the boundary of the target set
(the non-visibility region). In particular, we describe the transition between
the usable part of the boundary of the target (where the value vanishes) and
the non-usable part (where the value is positive). We show that the value
enjoys a different behaviour depending on the regularity of the obstacles
involved in the game. Namely, we prove that the boundary profile is continuous
for the case of smooth obstacles, and that it exhibits a jump discontinuity
when the obstacle contains corners. Moreover, we prove that, in the latter
case, there is a semi-permeable barrier emanating from the interface between
the usable and the non-usable part of the boundary of the target set.Comment: 33 pages, 8 figure
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