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Duration: 11 months
Starting date: July 2005
Status: Concluded
Project Contact:
(ca3@uninova.pt)

Scientific Advisors:
Manuel Barata, Rita Ribeiro, Pedro Sousa

MODI - Simulation of Knowledge Enabled Monitoring and Diagnosis Tool for Mars Lander Payloads (Monitoring and Diagnosis for Mars Driller) - Past Research Projects
Project Summary
Introduction

In the preparation of the ESA Aurora program, which targets the robotic and human exploration of our solar system bodies holding promise for traces of life, Mars has been naturally designed as the first step before organising more ambitious enterprises. On the basis of the technologies available in the next 20 or 30 years from now, by 2020-2030 an international human mission to Mars may be a reality [1].

Analysis conducted by NASA and other space agencies, as well and by an ESA internal ‘Group de reflection’, put in evidence that advanced Automation and Robotics are key mission enabling technologies for Mars exploration. These can be grouped into two classes: telerobotics, which is the remote computer-assisted manipulation of equipment and materials, and systems autonomy, which includes intelligent automated control, monitoring, diagnosis and re-planning. The salient feature of telerobotics is the direct control of the machine by a human operator, allowing simultaneous interaction with the fieldwork to be performed. However, the roundtrip radio time for Mars operations (up to a few tens of minutes in the case of direct communication; up to several days in the case of communication via an orbiting data relay) degrades and sometimes hinders the advantages of telepresence, which implies continuous sensory feedback to the operator.

To overcome this problem the robotic systems must carry sufficient sensors, effectors, and computer intelligence on board to perform simple tasks automatically, while higher level commands, as well as the selection and sequencing of tasks, are controlled by the teleoperator.

Autonomous machines constitute the next level of robot independence and are very probably the most suitable type for Mars surface applications.

On-board Monitoring & Diagnosis

The motivation for investigating Monitoring & Diagnosis technologies is based on the complex nature of remote exploration, characterised by restricted communication links and hostile environment. The uncertainty of the environment and the need for safety in response to low level anomalies limit the use of sequential tasking. Monitoring & Diagnosis is a base technology for autonomy, together with Planning & Scheduling, requiring AI techniques in order to reproduce the expertise of humans and performed advanced FDIR (Fault Detection Isolation and Recovery). Unmanned exploration units as well as the control units of manned space vehicles and habitats must be able to predict failures by monitoring their own evolving behaviour and detecting subtle deviations over time.

Monitoring & Diagnosis

Monitoring & Diagnosis capabilities have found little industrial application in space because of a lack of clear need, despite a good basis of theoretical studies on the usage of fuzzy logic or model-based FDIR. But a Monitoring & Diagnosis tools based on fuzzy logics is already running operational at ESOC, monitoring the gyroscopes of ENVISAT [2],[3]. This shows that the technology is mature if applied on well-known systems and implemented on ground. The suitability of Monitoring & Diagnosis in the frame of Aurora Mars missions is expected but still to be demonstrated: Can software monitor a complex mechanical devices of a robotic payload? If yes, can this be performed on-board?

MODI Objectives

The aim of the MODI activity is two answer these two questions, taking as a case study the Monitoring & Diagnosis of the most complex subsystem taken on board the payload of the two first ESA Exploration missions (ExoMars and Mars Sample Return) i.e. a drilling and sampling system.

The goal of this activity is thus to assess the feasibility of on-board or on-ground monitoring and diagnosis of devices of a drilling and sampling system (DSS) for Martian soil and to develop and demonstrate the MODI concept i.e. the benefits of a prototype Monitoring & Diagnosis software module (MDM) for this system. The software developed in the frame of this activity shall be of laboratory quality level.

System Overview

The following image depicts a first draft of MODI’s architecture, comprised by three main software modules:

  • DSS Simulator: Takes as input a set of Telecommands (with the intention to represent some demonstration scenario) and produces Telemetry according to that scenario.
  • MDM: Receives the input telemetry generated by the DSS Simulator and generates output information regarding the Monitoring and Diagnosis tasks.
  • Visualisation Module: Responsible for the generation of output Telecommands to operate the DSS Simulator and for displaying the telemetry values retrieved from the DSS Simulator and the Monitoring and Diagnosis results from the MDM Module.

Research Areas
Two main technologies will be used within the scope of MODI:
  • Rule Based Systems (containing the definition of rules for the detection of non-nominal behaviour)
  • Fuzzy Logic (applied to an Inference System, makes the rule definition flexible and suggestible to detect non-nominal trends impossible to trace using crisp values)
Partners

References
[1] Ribeiro, R., Pires, F., et al. (2002). Past & Future Of Knowledge Technologies: State of the Art and Roadmap for the Aurora Programme, UNINOVA/GTD - Cont. AO/1-4141/02/NL/LvH.

[2] Pires, F., Ribeiro, R., Pereira, A., Varas, Mantovani, G., Donati, A. (2002). Data quality fuzzy expert system. Proceedings of the 10th Mediterranean Conference on Control and Automation (MED02), Lisbon, Portugal.

[3] Pereira, A., Pires, F., Ribeiro, R., Correia, L., Viana, N., Varas, F., Mantovani, G., Bargellini, P., Perez-Bonilla, R., Donati, A. (2002) . Fuzzy expert system for gyroscope fault detection. Proceedings of the 16th European Simulation Multiconference, Artificial Intelligence and Neural Network Track, Darmstadt, Germany.

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