Pedro Henrique de Rodrigues Quemel e Assis Santana

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This work addresses the problem of stochastic state estimation for hybrid Markovian switching systems. The proposed Multiple Hypotheses Mixing Filter (MHMF) combines the Generalized Pseudo Bayes' (GPB) multiple hypotheses tracking with the Interacting Multiple Model's (IMM) estimates mixing in order to improve performance, the later being a particular case(More)
This work addresses the problem of stochastic data fusion for systems liable to heavy disturbances, which denote environmental perturbations strong enough to modify the system's internal structure, including signal interference, sensor faults, physical structure modification, and many other sources of disturbance. In these such cases, traditional filtering(More)
This work presents a new algorithm based on the Bucket Elimination framework that efficiently determines strong controllability of temporal plans formulated as Labeled Simple Temporal Networks with Uncertainty (LSTNU) with controllable and uncontrollable plan branches (choices).
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract Autonomous agents operating in partially observable stochastic environments often face the problem of optimizing expected performance while bounding the risk of violating safety constraints. Such problems can be modeled as(More)
Innovative methods have been developed for diagnosis, activity monitoring, and state estimation that achieve high accuracy through the use of stochastic models involving hybrid discrete and continuous behaviors. A key bottleneck is the automated acquisition of these hybrid models, and recent methods have focused predominantly on Jump Markov processes and(More)
Unmanned deep-sea and planetary vehicles operate in highly uncertain environments. Autonomous agents often are not adopted in these domains due to the risk of mission failure , and loss of vehicles. Prior work on contingent plan execution addresses this issue by placing bounds on uncertain variables and by providing consistency guarantees for a 'worst-case'(More)
We consider the problem of generating optimal stochastic policies for Constrained Stochastic Shortest Path problems, which are a natural model for planning under uncertainty for resource-bounded agents with multiple competing objectives. While unconstrained SSPs enjoy a multitude of efficient heuristic search solution methods with the ability to focus on(More)
In this demo, we will show how the Enterprise architecture, developed at the Model-based Embedded and Robotic Systems group over the past decades, can be used to control a mi-cro aerial vehicle, namely a Parrot ARDrone. The ARDrone is programmed using RMPL, the Reactive Model-based Programming Language, which allows a user to control the vehicle with(More)
A wide range of robotic missions contain activities that exhibit looping behaviour. Examples of these activities include picking fruit in agriculture, pick-and-place tasks in manufacturing, or even search patterns in robotic observing missions. These looping activities often have a range of acceptable loop values and a preference function over them. For(More)
This thesis focuses on the problem of temporal planning under uncertainty with explicit safety guarantees, which are enforced by means of chance constraints. We aim at elevating the level in which operators interact with autonomous agents and specify their desired behavior, while retaining a keen sensitivity to risk. Instead of relying on unconditional(More)