Unmanned Aerial Systems (UAS): Lessons learned relevant for the oil and gas industry

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1 Unmanned Aerial Systems (UAS): Lessons learned relevant for the oil and gas industry AUTONOMY IN IO 9-10 March 2011 Radisson BLU Atlantic Hotel, Stavanger Dr Andrew Lucas AOS Group Agent Oriented Software Pty Ltd,

2 Introduction Automation, Autonomy, Artificial Intelligence Air safety & integration of UAS into civil airspace Safety Assurance of an Autonomous Decision-making System (ADMS) Relevance to offshore oil industry 2

3 Automation, Autonomy & Artificial Intelligence System that has fixed choice points Programmed with a number of fixed alternative actions Take a passenger lift in a modern multi-storey building in Olso Inputs from particular sensors Passenger actions Lift computer evaluates these choice points without any consideration of the current circumstances We accept this mechanistic approach and do not expect or seek any adaptability, i.e. decision-making No explanation of its operation Inherent simplicity allows us to readily comprehend its actions; we trust and we rarely question it 3

4 Automation, Autonomy & Artificial Intelligence Need for decisions to be made at any time, with an appreciation for the circumstances of the current situation Should make a rational evaluation of the choices available and consider possible courses of action that could be taken We expect such a rational system to then make good decisions in terms of a human s assessment of those available choices As with humans, a decision-making system should behave in a proactive (i.e. goal-directed) or reactive (i.e. responsive) manner when making its decisions 4

5 Automation, Autonomy & Artificial Intelligence Does not mean out of control! 5

6 Automation, Autonomy & Artificial Intelligence Work for a human We delegate some authority to them, but they must work within these defined bounds 6

7 Autonomy Why Not Implement with Conventional Approaches? I.e. use well-established software development techniques proven over decades in automation applications These are complex, and are coded in Ada, C or C++ A feasible approach being used by some However, autonomy involves even further complexity and increased levels of abstraction Are there concepts & tools from last half century of research that may be of value? 7

8 Can Machines Think? Turing King s College, Cambridge One of the Bletchley Park code-breakers In 1950 proposed the Turing Test* Three people, A, B and C (the interrogator) communicate remotely by keyboard & printer If A is replaced by a computer and this is not apparent to C, then the computer, i.e. A, has succeeded in passing this test * A M Turing, Computing Machinery and Intelligence, reprinted in Minds and Machines, Ed A R Anderson, Prentice-Hall

9 Automation, Autonomy & Artificial Intelligence These concepts are very abstract, but what can computers do?* Solve geometric problems Learn Understand simple drawings Understand simple spoken languages Expert problem solving Useful industrial work Model psychological processes * P H Winston, MIT, Artificial Intelligence, Addison Wesley

10 Introduce the Concept of Agency Michael Bratman, philospher, of Stanford University Developed the theory of rational human action Based upon practical reasoning Used the concept of intentions to reduce number of possibilities to be considered when planning the next action The basis for the science of rational agents 10

11 Rational Agents An agent is an entity that persists in, and acts upon, the environment it inhabits Humans are agents, but are often irrational A rational agent acts in its own best interests, given its knowledge of the world around it An agent has the following properties: Autonomy Proactivity Reactivity Social ability Ref. Wooldridge Reasoning about Rational Agents 11

12 Rational human reasoning BDI Rational Agent Beliefs - perceived understanding of the world Beliefs database of perceived world knowledge Goals or desires Execution Engine handles control of execution of intentions Goals or Desires Intentions plans currently executing (commitment) Accumulated experience and behaviours Behaviours - pre-compiled plans 12

13 Realisation of BDI Agent Systems SRI International 1987 Developed in response to NASA interest C-PRS/Open LAAS/CNRS early 90s dmars AOS AOS Aimed at Safety-critical systems market 13

14 Autonomous Decision-making System ADMS provides capability that underpins autonomy, consequently: Safe execution engine Appropriate decision-making behaviours that are safety assured Ability to add or modify behaviours with minimal need to re-clear the execution engine Accurate data 14

15 Potential Aviation Applications of Autonomy Autonomous UAS, for a variety of civilian applications Support for the crew in manned aircraft Air traffic management 15

16 Current Status Raising Level of Maturity 16

17 What is Acceptable Air Safety? European Aviation Safety Agency (EASA) and US Federal Aviation Agency (FAA) have generally harmonised safety regulations for manned passenger aircraft CS25/FAR 25 for large aircraft CS23/FAR 23 for commuter aircraft under 8618 kg ( lb) 17

18 Large Aircraft Safety CS/FAR 25 Historical evidence probability of a serious accident due to operational and airframe-related causes was approximately one per million hours of flight (or 10-6 ) 10-6 Ref. CS-25, Book 2, Acceptable Means of Compliance, Page 2-F-39 18

19 Systems Contribution about 10 per cent of the total were attributed to Failure Conditions caused by the aeroplane s systems It is reasonable to expect that the probability of a serious accident from all such Failure Conditions be not greater than one per ten million flight hours or 1 x 10-7 per flight hour for a newly designed aeroplane 10-7 Ref. CS-25, Book 2, Acceptable Means of Compliance, Page 2-F-39 19

20 Per System We can t say whether the target has been met until all systems are collectively analysed numerically It is assumed (arbitrarily) that there are about one hundred potential Failure Conditions in an aeroplane, which could be Catastrophic The target allowable Average Probability per Flight Hour of 1 x 10-7 when apportioned equally among these, results in 1 x 10-9 for each failure condition 10-9 Ref. CS-25, Book 2, Acceptable Means of Compliance, Page 2-F-39 20

21 Acceptable Levels for Catastrophic Failure Based upon this rationale, CS25 concludes The upper limit for the Average Probability per Flight Hour for Catastrophic Failure Conditions would be 1 x 10-9, which establishes an approximate probability value for the term Extremely Improbable Catastrophic is defined as - Failure Conditions, which would result in multiple fatalities, usually with the loss of the aeroplane Ref. CS-25, Book 2, Acceptable Means of Compliance, Page 2-F-39 21

22 Failure Mode Severities vs Probability Classification of failure conditions Allowable qualitative probability Allowable Quantitative probability per Flight Hour on the order of.! No safety Effect Minor Major Hazardous Catastrophic No Probability Requirement No Probability Requirement Probable Remote Extremely Remote <10-3 Note 1 Extremely Improbable <10-5 <10-7 <10-9 Non Detect and Avoid Equipped. Detect and Avoid Equipped. Note 2 Software/CEH Dev Level D Software/CEH Dev Level C Software/CEH Dev Level B Software/CEH Dev Level A 22

23 Introducing Unmanned Air Systems into Civil Airspace Note! No current RPAVs certified to a civil standard Global Hawk, Predator UAS must not increase probability of large aircraft catastrophic accident due to operational and airframe-related causes i.e. approximately one per million hours of flight (or 10-6 ) Depending upon the number of systems on UAS, consequence for UAS systems safety is a target in the range 10-7 to

24 Civil Air Transport Safety Statistics Static over the last 10 years, no substantial improvement Human factor a substantial contributor 24

25 UK Regulatory Initiatives for Autonomous UAS 2008 CAA updated CAP722 Unmanned Aircraft System Operations in UK Airspace Guidance Autonomy introduced for first time in a regulatory document CAP 722 Unmanned Aircraft System Operations in UK Airspace Guidance Chapter 7 UAS Autonomy 1 Scope 1.1 This guidance relates to the autonomy of civil unmanned aircraft systems. 2 Lead Agency 2.1 The lead agencies are as for Section 3, Chapter 2, Certification. 3 Introduction 3.1 The concept of an autonomous UAS is a system that will do everything for itself. It will be able to follow the planned route, communicate with Aircraft Controllers and other airspace users, detect, diagnose and recover from faults, and operate at least as safely as a system with continuous human involvement and control. 3.2 Definition of Autonomy Autonomy is the capability of the system to make decisions based upon an evaluation of the current situation (often referred to as situation awareness). The system must 2009 CAA first take released account of situational a awareness draft data that of is pertinent AMC to the decision UAS.1309: about to Guidance be made. Autonomous systems should make a rational evaluation of the choices available and the possible courses of action that could be taken, in light of this material for UAS system safety requirements situation awareness in order to make its decision. We expect such a rational system to then make good decisions in terms of a human s assessment of those available choices To perform in this way, an autonomous system will accept sensor and user inputs as an automatic system would. However, it will operate with more abstract concepts, rather than reacting only to inputs in a fixed manner irrespective of the current 25

26 AeroSynergy Proposed Safety Assurance Process for ADMS Establish a basis of certification, agreed with the CAA Draft a Certification Plan, review with CAA Functional Hazard Assessment FMECA Reliability assessment Safety targets per AMC (UAS)

27 ASTRAEA Virtual Certification To reduce risk for commercial development of a UAS Through a virtual process, plot and document route to UAS certification Forum for CAA to comment upon & provide guidance towards acceptable technical and regulatory framework Virtual Pilot or ADMS (Autonomous Decision-making System) a key component Based on an A/C already certified to CS23 27

28 Relevance to the Offshore Oil Industry Autonomous systems are not simply extensions of automatic systems have a different conceptual basis Potential for decision-making software to provide autonomous capability for key exploration & production functions Offers the potential to reduce accidents and incidents where human error occurs Consequently they must be safety assured Safety assurance for ADMS in the oil industry can benefit from the technologies and regulations currently being developed in aviation 28

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