Pedro Henrique de Rodrigues Quemel e Assis Santana

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— 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(More)
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 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).
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 chance-constrained POMDP's (CC-POMDP's). Our first contribution is a systematic derivation of execution risk in POMDP domains, which(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)
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)
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)
Inspired by risk-sensitive, robust scheduling for planetary rovers under temporal uncertainty, this work introduces the Probabilistic Simple Temporal Network with Uncertainty (PSTNU), a temporal planning formalism that unifies the set-bounded and probabilistic temporal uncertainty models from the STNU and PSTN literature. By allowing any combination of(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)
— Fault diagnosis and recovery are essential tools for the development of autonomous agents that can operate in hazardous environments. This can be effectively approached from a model-based perspective, where sensor faults are explicitly taken into account in a hybrid model with switching dynamics. However, practical hybrid filters are required to manage an(More)