Pedro Henrique de Rodrigues Quemel e Assis Santana

I-dual: Solving Constrained SSPs via Heuristic Search in the Dual Space

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 resourcebounded agents with multiple competing objectives. While unconstrained SSPs enjoy a multitude of efficient heuristic search solution methods with the ability to focus on promising areas reachable from the initial state, the state of the art for constrained SSPs revolves around linear and dynamic programming algorithms which explore the entire state space. In this paper, we present i-dual, the first heuristic search algorithm for constrained SSPs. To concisely represent constraints and efficiently decide their violation, i-dual operates in the space of dual variables describing the policy occupation measures. It does so while retaining the ability to use standard value function heuristics computed by well-known methods. Our experiments show that these features enable i-dual to achieve up to two orders of magnitude improvement in run-time and memory over linear programming algorithms.

A new filter for hybrid systems and its applications to robust attitude estimation

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 exponential growth in the number of discrete mode sequences, also known as hypotheses. Inspired by an attitude estimation application for a quadrotor UAV with faulty sensors, this paper introduces the IP-MHMF, a novel filter for hybrid systems that generalizes the well-known IMM and introduces a more informed hypothesis-pruning step than previous algorithms. By performing hypothesis pruning on corrected rather than predicted hypothesis probabilities, the IP-MHMF is capable of much more aggressive pruning strategies that significantly reduce its computational load, while improving its estimation performance. Our numerical results on data from a real robotic platform show that the IP-MHMF outperforms state-of-the-art hybrid filters and the traditional EKF on an attitude estimation application with faulty magnetometer measurements.

Scaled Minimum Unscented Multiple Hypotheses Mixing Filter

This work brings two new contributions. First, it introduces the Scaled Minimum Unscented Multiple Hypotheses Mixing Filter, a novel filter for hybrid dynamical systems that 1) uses a new minimum set of sigma points along with the scaled unscented transform in a hybrid framework; 2) can estimate the Markovian Transition Probability Matrix in real-time; 3) features a pruning step that reduces the filters computational effort and prevents its estimates from being degraded by very unlikely hypotheses; and 4) has a mixing step with merging depth greater than one. Second, we present a result revealing the conservativeness of one of the scaled unscented transform forms.