AUTONOMOUS ROVER PATH PLANNING AND ... - European Space...
Transcript of AUTONOMOUS ROVER PATH PLANNING AND ... - European Space...
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AUTONOMOUS ROVER PATH PLANNING AND RECONFIGURATION
J. Graciano, E. Chester
11th Symposium on Advanced Space Technologies in Robotics and AutomationESTEC, 11‐15 April 2011
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AEVO GmbH
R&D company with HQ in Gilching/Oberpfaffenhofen (Munich)
Founded in March 2010 in the framework of the ESA BIC Initiative
Focus on technology transfer of computer‐based optimisation
Projects in the areas of: Component design
Communication scheduling
Mission design
Logistics
Member of bavAIRia
COMPANY
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DESIGNTools that explore the system
design space to findthe most robust options.
SCHEDULETools that optimise
task or team schedulesto improve resource usage.
OPTIMISATION ENGINES
DEVELOPTools that find the best trade‐off
between conflicting objectives without violating the constraints.
OPTIMISETools that fine‐tune a system
calibration to increaseefficiency and representation quality.
OPTIMISATIONENGINES
AEVO's Optimisation Engines are tools that optimise the process ofdesigning, developing, testing and fine‐tuning systems.
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TECHNOLOGIES
Algorithms
Interpolation, Least Squares
Gradient Methods
Monte‐Carlo Algorithms
Stochastic Search
Game Theory
Evolutionary Methods
Neural Networks
(...)
Software
Matlab/Simulink
SciLab
Fortran
C/C++
Perl
XML
(...)
OPTIMISATIONENGINES
AEVO's Optimisation Enginesuse different methods and algorithms for each specific problem
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Multi‐user, multi‐objective, multi‐mission spacecraft and ground segment scheduling tool
MOSATS
Objective:Optimisation of the satellite communication windows for multiple ground stations and multiple satellites. This is a highly constrained problem, where the objectives incide on the constraint satisfiability and on the maximisation of scientific return.
Key advantages:
Easy to use, web‐based front‐end
Versatile integration support with existing tools through use of XSLT/XPath transformations
Support for ESA‐ESOC, NASA‐JPL, and AGI’s STK data formats
Rapid scenario definition and disruption assessment
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Scheduling Infrastructure for Resource Optimisation
SIROCCO
Objective:In the framework of a validation project with support of the DLR‐GSOC, AEVO is developing a software for the resource optimisation of spacecraft on‐board tasks. The objective is to plan and schedule all necessary tasks in a defined time period, arising from 2 sources: payload operations requests and operating rules and procedures related to the operations.
Some tasks considered are: Uplinks
Payload Operations
Downlinks
File deletion
Sleep phases
Attitude manoeuvres
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Parametric Entry, Descent and Landing Synthesis
PEDALS
Objective:Demonstration of a design‐by‐evolution approach for developing mission architectures for entry vehicles.
Aspects considered: Aerodynamic shape optimisation
Atmospheric flight simulation
Multiple decelerator concepts
Parachute/Ballute/Thruster sizing
Sleep phases
Attitude changed
Integration of MOLA reference topology
Integration of Mars‐GRAM engineeringatmosphere model of Mars
Demonstrate concept validity without use ofITAR‐restricted codes
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RATIONALE & SCOPERationaleEfficient and confident route planning for rover operationsAdaptive re‐planning (reacts to introduction of other actors, changes in mission, changing priorities, terrain hazards, etc.)Continuous trajectory design
Scope
Development of minima‐free artificial potential (AP) method
Validation for realistic scenarios
Simulation work to examine flexibility and suitability of the method in dealing with:
‐ Static conflicts (obstacle avoidance)‐ Dynamic conflicts (re‐planning)‐ Targeting
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MODELLING (1)ARTIFICIAL POTENTIALS
Obstacles are represented by repulsive potentials
Goals represented by attractive potentials
Total potential field is built through weighted sum of all individual APs
Gradient of total field is used to navigate, from
high potential states to low potential states
Drawback: unless functions are carefullychosen, the potential field suffers fromlocal minima, causing undesired stops
Schematic potential field withconvergence and avoidance sets [2]
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MODELLING (2)GAMES
Mathematical modeling of situations of interaction involving two or more agents
Usually comprises [3]:‐ set of players
‐ set of possible moves
‐ payoffs awarded to each player in function of all moves combinations
‐ rules of the game (may include move precedence, restrictions on memory)
Nash equilibrium: no incentive to unilateral departure
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MODELLING (3)Orthogonality between "players" Target gradient field
Obstacle gradient field Total gradient field
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MODELLING (4)Functions
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RESULTSFIELD INTENSITY (AVOIDANCE STRENGTH)
F = 0.2 F = 2.0
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RESULTSFIELD TYPE
(Regions to avoid
are red, to prefer
are green)
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RESULTSFIELD FUNCTION
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RESULTSFIELD FUNCTION
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RESULTSFIELD FUNCTION
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RESULTSFIELD FUNCTION
R = 1
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RESULTSFIELD INTENSITY
R = 0.5 R = 1.5
(demo)
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RESULTS
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FURTHER ACTIVITIESDevelopment
Implementation (demonstration project)
Improve modelling (3D, realistic dynamics and constraints)
Additional features (shadow of the future)
Applications
Planetary rovers
On‐Orbiting servicing / docking
UAV (single, formation)
General robot routing
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SUMMARY Demonstration of path planning and path optimisation for platforms in
the presence of multiple obstacles and terrain hazards.
Main advantages: No "local‐minima" problem
Rapid, robust algorithm suitable for embedded navigation executive
Broader use of potential functions
Aspect analysed: Static conflicts
Dynamic conflicts
Path re‐planning in dynamic environment
Selective target activation (for autonomous science planning)
Also:
Chase/escape situations
Formation flying / team motion
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João Graciano
AEVO GmbHFriedrichshafenerstrasse 182205 GilchingTel: +49 8105 772 7757Fax: +49 8105 772 7755
QUESTIONS?
Thank you for your attention
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FORMATION FLIGHT