Elodie Chanthery

Monitoring and Active Diagnosis for Discrete-Event Systems

Abstract This article presents an original way to enrich the monitoring of discrete-event systems named active diagnosis. The objective of on-line active diagnosis is to find an admissible sequence of actions (or plan) that refines the diagnosis without radically changing the mission plan. This paper has two major contributions. First, active diagnosis is formally defined in the finite-state automata theory framework. This leads to the definition of a complete active diagnoser which on line monitors the system behavior. Secondly, from the complete active diagnoser is defined a planning problem. The goal is to find a conditional plan that defines an admissible sequence of actions. These actions are applied on the physical system and may conduct the active diagnoser into a diagnosable region.

Planning algorithms for autonomous aerial vehicle

Abstract Planning function is essential for increasing the autonomy of aerial systems. This paper presents some improvements dedicated to the management of degraded events in an existing control architecture. These events may start an online replanning. In a military observation mission context, a complex modeling and several efficient algorithms for planning are proposed. Experiments highlight the efficiency of these solutions.

Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets

This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype. The diagnosis is performed by using a model-based method in the context of health management of hybrid systems. In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

Mission planning for autonomous aerial vehicles

Abstract This paper presents a planning of a military observation mission for one autonomous aerial vehicle. The mission is modeled by a directed graph. The planning aims at finding a path in this graph and a speed for each arc of the path. The criterion is computed using a nonlinear function of the flight duration, constrained by the vehicle motion, danger and fuel quantity. An algorithm based on an ordered depth-first search with an embedded Frank-Wolfe algorithm is proposed. Numerical experiments highlight the feasibility and the flexibility of the approach.

Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover

This chapter presents a new methodology to perform health monitoring of hybrid systems under uncertainty. Hybrid systems can be represented as multi-mode systems with hybrid automata. Diagnosers are generated from these hybrid automata using a new data structure in order to monitor both the behavior and degradation of such systems. After a review of the state of the art on different existing solutions for diagnosis of hybrid systems under uncertainty, we propose to introduce the Hybrid Particle Petri Nets (HPPN) modeling framework. The main advantage of HPPN is that they take into account knowledge-based uncertainty in the system representation and uncertainty in the diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as fault events) and it is robust to false observations. It also estimates the continuous state of the system by using particle filtering. A methodology is proposed to perform model-based diagnosis on hybrid systems by using the HPPN modeling framework. The system diagnosis is computed at any time from a HPPN-based diagnoser and contains all the hypotheses over its past mode trajectory. Each hypothesis is valued with a belief degree and includes discrete and continuous state estimates, as well as the set of faults that occurred on the system up to the current time. The HPPN-based methodology is demonstrated with an application on the K11 planetary rover prototype developed by NASA Ames Research Center. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

Formalizing and Solving Information Collection Problems with Autonomous Sensor Systems

This paper addresses the problem of planning data collection missions for a set of Information Collection Systems (ICS) to respond to a set of information requests. This problem goes from the formalization of information needs to the optimization of ICS actions. After having formalized requests and decomposed them into elementary requests, the problem can be modeled with a graph characterizing the various aspects: coordination and assignment of ICSs, request satisfaction and ICS use optimization. Based on this graph, the problem can be solved with an A*-like search algorithm.